Class: AWS.SageMaker
- Inherits:
-
AWS.Service
- Object
- AWS.Service
- AWS.SageMaker
- Identifier:
- sagemaker
- API Version:
- 2017-07-24
- Defined in:
- (unknown)
Overview
Constructs a service interface object. Each API operation is exposed as a function on service.
Service Description
Provides APIs for creating and managing SageMaker resources.
Other Resources:
Sending a Request Using SageMaker
var sagemaker = new AWS.SageMaker();
sagemaker.addAssociation(params, function (err, data) {
if (err) console.log(err, err.stack); // an error occurred
else console.log(data); // successful response
});
Locking the API Version
In order to ensure that the SageMaker object uses this specific API, you can
construct the object by passing the apiVersion
option to the constructor:
var sagemaker = new AWS.SageMaker({apiVersion: '2017-07-24'});
You can also set the API version globally in AWS.config.apiVersions
using
the sagemaker service identifier:
AWS.config.apiVersions = {
sagemaker: '2017-07-24',
// other service API versions
};
var sagemaker = new AWS.SageMaker();
Version:
-
2017-07-24
Waiter Resource States
This service supports a list of resource states that can be polled using the waitFor() method. The resource states are:
notebookInstanceInService, notebookInstanceStopped, notebookInstanceDeleted, trainingJobCompletedOrStopped, endpointInService, endpointDeleted, transformJobCompletedOrStopped, processingJobCompletedOrStopped, imageCreated, imageUpdated, imageDeleted, imageVersionCreated, imageVersionDeleted
Constructor Summary collapse
-
new AWS.SageMaker(options = {}) ⇒ Object
constructor
Constructs a service object.
Property Summary collapse
-
endpoint ⇒ AWS.Endpoint
readwrite
An Endpoint object representing the endpoint URL for service requests.
Properties inherited from AWS.Service
Method Summary collapse
-
addAssociation(params = {}, callback) ⇒ AWS.Request
Creates an association between the source and the destination.
-
addTags(params = {}, callback) ⇒ AWS.Request
Adds or overwrites one or more tags for the specified SageMaker resource.
-
associateTrialComponent(params = {}, callback) ⇒ AWS.Request
Associates a trial component with a trial.
-
batchDescribeModelPackage(params = {}, callback) ⇒ AWS.Request
This action batch describes a list of versioned model packages
.
-
createAction(params = {}, callback) ⇒ AWS.Request
Creates an action.
-
createAlgorithm(params = {}, callback) ⇒ AWS.Request
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
.
-
createApp(params = {}, callback) ⇒ AWS.Request
Creates a running app for the specified UserProfile.
-
createAppImageConfig(params = {}, callback) ⇒ AWS.Request
Creates a configuration for running a SageMaker image as a KernelGateway app.
-
createArtifact(params = {}, callback) ⇒ AWS.Request
Creates an artifact.
-
createAutoMLJob(params = {}, callback) ⇒ AWS.Request
Creates an Autopilot job.
Find the best-performing model after you run an Autopilot job by calling .
For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
. -
createCodeRepository(params = {}, callback) ⇒ AWS.Request
Creates a Git repository as a resource in your SageMaker account.
-
createCompilationJob(params = {}, callback) ⇒ AWS.Request
Starts a model compilation job.
-
createContext(params = {}, callback) ⇒ AWS.Request
Creates a context.
-
createDataQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates a definition for a job that monitors data quality and drift.
-
createDeviceFleet(params = {}, callback) ⇒ AWS.Request
Creates a device fleet.
.
-
createDomain(params = {}, callback) ⇒ AWS.Request
Creates a
Domain
used by Amazon SageMaker Studio. -
createEdgePackagingJob(params = {}, callback) ⇒ AWS.Request
Starts a SageMaker Edge Manager model packaging job.
-
createEndpoint(params = {}, callback) ⇒ AWS.Request
Creates an endpoint using the endpoint configuration specified in the request.
-
createEndpointConfig(params = {}, callback) ⇒ AWS.Request
Creates an endpoint configuration that SageMaker hosting services uses to deploy models.
-
createExperiment(params = {}, callback) ⇒ AWS.Request
Creates an SageMaker experiment.
-
createFeatureGroup(params = {}, callback) ⇒ AWS.Request
Create a new
FeatureGroup
. -
createFlowDefinition(params = {}, callback) ⇒ AWS.Request
Creates a flow definition.
.
-
createHumanTaskUi(params = {}, callback) ⇒ AWS.Request
Defines the settings you will use for the human review workflow user interface.
-
createHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Starts a hyperparameter tuning job.
-
createImage(params = {}, callback) ⇒ AWS.Request
Creates a custom SageMaker image.
-
createImageVersion(params = {}, callback) ⇒ AWS.Request
Creates a version of the SageMaker image specified by
ImageName
. -
createInferenceRecommendationsJob(params = {}, callback) ⇒ AWS.Request
Starts a recommendation job.
-
createLabelingJob(params = {}, callback) ⇒ AWS.Request
Creates a job that uses workers to label the data objects in your input dataset.
-
createModel(params = {}, callback) ⇒ AWS.Request
Creates a model in SageMaker.
-
createModelBiasJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates the definition for a model bias job.
.
-
createModelExplainabilityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates the definition for a model explainability job.
.
-
createModelPackage(params = {}, callback) ⇒ AWS.Request
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(params = {}, callback) ⇒ AWS.Request
Creates a model group.
-
createModelQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates a definition for a job that monitors model quality and drift.
-
createMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
.
-
createNotebookInstance(params = {}, callback) ⇒ AWS.Request
Creates an SageMaker notebook instance.
-
createNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Creates a lifecycle configuration that you can associate with a notebook instance.
-
createPipeline(params = {}, callback) ⇒ AWS.Request
Creates a pipeline using a JSON pipeline definition.
.
-
createPresignedDomainUrl(params = {}, callback) ⇒ AWS.Request
Creates a URL for a specified UserProfile in a Domain.
-
createPresignedNotebookInstanceUrl(params = {}, callback) ⇒ AWS.Request
Returns a URL that you can use to connect to the Jupyter server from a notebook instance.
-
createProcessingJob(params = {}, callback) ⇒ AWS.Request
Creates a processing job.
.
-
createProject(params = {}, callback) ⇒ AWS.Request
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.
.
-
createStudioLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Creates a new Studio Lifecycle Configuration.
.
-
createTrainingJob(params = {}, callback) ⇒ AWS.Request
Starts a model training job.
-
createTransformJob(params = {}, callback) ⇒ AWS.Request
Starts a transform job.
-
createTrial(params = {}, callback) ⇒ AWS.Request
Creates an SageMaker trial.
-
createTrialComponent(params = {}, callback) ⇒ AWS.Request
Creates a trial component, which is a stage of a machine learning trial.
-
createUserProfile(params = {}, callback) ⇒ AWS.Request
Creates a user profile.
-
createWorkforce(params = {}, callback) ⇒ AWS.Request
Use this operation to create a workforce.
-
createWorkteam(params = {}, callback) ⇒ AWS.Request
Creates a new work team for labeling your data.
-
deleteAction(params = {}, callback) ⇒ AWS.Request
Deletes an action.
.
-
deleteAlgorithm(params = {}, callback) ⇒ AWS.Request
Removes the specified algorithm from your account.
.
-
deleteApp(params = {}, callback) ⇒ AWS.Request
Used to stop and delete an app.
.
-
deleteAppImageConfig(params = {}, callback) ⇒ AWS.Request
Deletes an AppImageConfig.
.
-
deleteArtifact(params = {}, callback) ⇒ AWS.Request
Deletes an artifact.
-
deleteAssociation(params = {}, callback) ⇒ AWS.Request
Deletes an association.
.
-
deleteCodeRepository(params = {}, callback) ⇒ AWS.Request
Deletes the specified Git repository from your account.
.
-
deleteContext(params = {}, callback) ⇒ AWS.Request
Deletes an context.
.
-
deleteDataQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes a data quality monitoring job definition.
.
-
deleteDeviceFleet(params = {}, callback) ⇒ AWS.Request
Deletes a fleet.
.
-
deleteDomain(params = {}, callback) ⇒ AWS.Request
Used to delete a domain.
-
deleteEndpoint(params = {}, callback) ⇒ AWS.Request
Deletes an endpoint.
-
deleteEndpointConfig(params = {}, callback) ⇒ AWS.Request
Deletes an endpoint configuration.
-
deleteExperiment(params = {}, callback) ⇒ AWS.Request
Deletes an SageMaker experiment.
-
deleteFeatureGroup(params = {}, callback) ⇒ AWS.Request
Delete the
FeatureGroup
and any data that was written to theOnlineStore
of theFeatureGroup
. -
deleteFlowDefinition(params = {}, callback) ⇒ AWS.Request
Deletes the specified flow definition.
.
-
deleteHumanTaskUi(params = {}, callback) ⇒ AWS.Request
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 .
-
deleteImage(params = {}, callback) ⇒ AWS.Request
Deletes a SageMaker image and all versions of the image.
-
deleteImageVersion(params = {}, callback) ⇒ AWS.Request
Deletes a version of a SageMaker image.
-
deleteModel(params = {}, callback) ⇒ AWS.Request
Deletes a model.
-
deleteModelBiasJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes an Amazon SageMaker model bias job definition.
.
-
deleteModelExplainabilityJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes an Amazon SageMaker model explainability job definition.
.
-
deleteModelPackage(params = {}, callback) ⇒ AWS.Request
Deletes a model package.
A model package is used to create SageMaker models or list on Amazon Web Services Marketplace.
-
deleteModelPackageGroup(params = {}, callback) ⇒ AWS.Request
Deletes the specified model group.
.
-
deleteModelPackageGroupPolicy(params = {}, callback) ⇒ AWS.Request
Deletes a model group resource policy.
.
-
deleteModelQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes the secified model quality monitoring job definition.
.
-
deleteMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Deletes a monitoring schedule.
-
deleteNotebookInstance(params = {}, callback) ⇒ AWS.Request
Deletes an SageMaker notebook instance.
-
deleteNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Deletes a notebook instance lifecycle configuration.
.
-
deletePipeline(params = {}, callback) ⇒ AWS.Request
Deletes a pipeline if there are no running instances of the pipeline.
-
deleteProject(params = {}, callback) ⇒ AWS.Request
Delete the specified project.
.
-
deleteStudioLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Deletes the Studio Lifecycle Configuration.
-
deleteTags(params = {}, callback) ⇒ AWS.Request
Deletes the specified tags from an SageMaker resource.
To list a resource's tags, use the
ListTags
API. -
deleteTrial(params = {}, callback) ⇒ AWS.Request
Deletes the specified trial.
-
deleteTrialComponent(params = {}, callback) ⇒ AWS.Request
Deletes the specified trial component.
-
deleteUserProfile(params = {}, callback) ⇒ AWS.Request
Deletes a user profile.
-
deleteWorkforce(params = {}, callback) ⇒ AWS.Request
Use this operation to delete a workforce.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.
If a private workforce contains one or more work teams, you must use the operation to delete all work teams before you delete the workforce.
-
deleteWorkteam(params = {}, callback) ⇒ AWS.Request
Deletes an existing work team.
-
deregisterDevices(params = {}, callback) ⇒ AWS.Request
Deregisters the specified devices.
-
describeAction(params = {}, callback) ⇒ AWS.Request
Describes an action.
.
-
describeAlgorithm(params = {}, callback) ⇒ AWS.Request
Returns a description of the specified algorithm that is in your account.
.
-
describeApp(params = {}, callback) ⇒ AWS.Request
Describes the app.
.
-
describeAppImageConfig(params = {}, callback) ⇒ AWS.Request
Describes an AppImageConfig.
.
-
describeArtifact(params = {}, callback) ⇒ AWS.Request
Describes an artifact.
.
-
describeAutoMLJob(params = {}, callback) ⇒ AWS.Request
Returns information about an Amazon SageMaker AutoML job.
.
-
describeCodeRepository(params = {}, callback) ⇒ AWS.Request
Gets details about the specified Git repository.
.
-
describeCompilationJob(params = {}, callback) ⇒ AWS.Request
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob.
-
describeContext(params = {}, callback) ⇒ AWS.Request
Describes a context.
.
-
describeDataQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Gets the details of a data quality monitoring job definition.
.
-
describeDevice(params = {}, callback) ⇒ AWS.Request
Describes the device.
.
-
describeDeviceFleet(params = {}, callback) ⇒ AWS.Request
A description of the fleet the device belongs to.
.
-
describeDomain(params = {}, callback) ⇒ AWS.Request
The description of the domain.
.
-
describeEdgePackagingJob(params = {}, callback) ⇒ AWS.Request
A description of edge packaging jobs.
.
-
describeEndpoint(params = {}, callback) ⇒ AWS.Request
Returns the description of an endpoint.
.
-
describeEndpointConfig(params = {}, callback) ⇒ AWS.Request
Returns the description of an endpoint configuration created using the
CreateEndpointConfig
API..
-
describeExperiment(params = {}, callback) ⇒ AWS.Request
Provides a list of an experiment's properties.
.
-
describeFeatureGroup(params = {}, callback) ⇒ AWS.Request
Use this operation to describe a
FeatureGroup
. -
describeFlowDefinition(params = {}, callback) ⇒ AWS.Request
Returns information about the specified flow definition.
.
-
describeHumanTaskUi(params = {}, callback) ⇒ AWS.Request
Returns information about the requested human task user interface (worker task template).
.
-
describeHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Gets a description of a hyperparameter tuning job.
.
-
describeImage(params = {}, callback) ⇒ AWS.Request
Describes a SageMaker image.
.
-
describeImageVersion(params = {}, callback) ⇒ AWS.Request
Describes a version of a SageMaker image.
.
-
describeInferenceRecommendationsJob(params = {}, callback) ⇒ AWS.Request
Provides the results of the Inference Recommender job.
-
describeLabelingJob(params = {}, callback) ⇒ AWS.Request
Gets information about a labeling job.
.
-
describeLineageGroup(params = {}, callback) ⇒ AWS.Request
Provides a list of properties for the requested lineage group.
-
describeModel(params = {}, callback) ⇒ AWS.Request
Describes a model that you created using the
CreateModel
API..
-
describeModelBiasJobDefinition(params = {}, callback) ⇒ AWS.Request
Returns a description of a model bias job definition.
.
-
describeModelExplainabilityJobDefinition(params = {}, callback) ⇒ AWS.Request
Returns a description of a model explainability job definition.
.
-
describeModelPackage(params = {}, callback) ⇒ AWS.Request
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
. -
describeModelPackageGroup(params = {}, callback) ⇒ AWS.Request
Gets a description for the specified model group.
.
-
describeModelQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Returns a description of a model quality job definition.
.
-
describeMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Describes the schedule for a monitoring job.
.
-
describeNotebookInstance(params = {}, callback) ⇒ AWS.Request
Returns information about a notebook instance.
.
-
describeNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Returns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
. -
describePipeline(params = {}, callback) ⇒ AWS.Request
Describes the details of a pipeline.
.
-
describePipelineDefinitionForExecution(params = {}, callback) ⇒ AWS.Request
Describes the details of an execution's pipeline definition.
.
-
describePipelineExecution(params = {}, callback) ⇒ AWS.Request
Describes the details of a pipeline execution.
.
-
describeProcessingJob(params = {}, callback) ⇒ AWS.Request
Returns a description of a processing job.
.
-
describeProject(params = {}, callback) ⇒ AWS.Request
Describes the details of a project.
.
-
describeStudioLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Describes the Studio Lifecycle Configuration.
.
-
describeSubscribedWorkteam(params = {}, callback) ⇒ AWS.Request
Gets information about a work team provided by a vendor.
-
describeTrainingJob(params = {}, callback) ⇒ AWS.Request
Returns information about a training job.
-
describeTransformJob(params = {}, callback) ⇒ AWS.Request
Returns information about a transform job.
.
-
describeTrial(params = {}, callback) ⇒ AWS.Request
Provides a list of a trial's properties.
.
-
describeTrialComponent(params = {}, callback) ⇒ AWS.Request
Provides a list of a trials component's properties.
.
-
describeUserProfile(params = {}, callback) ⇒ AWS.Request
Describes a user profile.
-
describeWorkforce(params = {}, callback) ⇒ AWS.Request
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs).
-
describeWorkteam(params = {}, callback) ⇒ AWS.Request
Gets information about a specific work team.
-
disableSagemakerServicecatalogPortfolio(params = {}, callback) ⇒ AWS.Request
Disables using Service Catalog in SageMaker.
-
disassociateTrialComponent(params = {}, callback) ⇒ AWS.Request
Disassociates a trial component from a trial.
-
enableSagemakerServicecatalogPortfolio(params = {}, callback) ⇒ AWS.Request
Enables using Service Catalog in SageMaker.
-
getDeviceFleetReport(params = {}, callback) ⇒ AWS.Request
Describes a fleet.
.
-
getLineageGroupPolicy(params = {}, callback) ⇒ AWS.Request
The resource policy for the lineage group.
.
-
getModelPackageGroupPolicy(params = {}, callback) ⇒ AWS.Request
Gets a resource policy that manages access for a model group.
-
getSagemakerServicecatalogPortfolioStatus(params = {}, callback) ⇒ AWS.Request
Gets the status of Service Catalog in SageMaker.
-
getSearchSuggestions(params = {}, callback) ⇒ AWS.Request
An auto-complete API for the search functionality in the Amazon SageMaker console.
-
listActions(params = {}, callback) ⇒ AWS.Request
Lists the actions in your account and their properties.
.
-
listAlgorithms(params = {}, callback) ⇒ AWS.Request
Lists the machine learning algorithms that have been created.
.
-
listAppImageConfigs(params = {}, callback) ⇒ AWS.Request
Lists the AppImageConfigs in your account and their properties.
-
listApps(params = {}, callback) ⇒ AWS.Request
Lists apps.
.
-
listArtifacts(params = {}, callback) ⇒ AWS.Request
Lists the artifacts in your account and their properties.
.
-
listAssociations(params = {}, callback) ⇒ AWS.Request
Lists the associations in your account and their properties.
.
-
listAutoMLJobs(params = {}, callback) ⇒ AWS.Request
Request a list of jobs.
.
-
listCandidatesForAutoMLJob(params = {}, callback) ⇒ AWS.Request
List the candidates created for the job.
.
-
listCodeRepositories(params = {}, callback) ⇒ AWS.Request
Gets a list of the Git repositories in your account.
.
-
listCompilationJobs(params = {}, callback) ⇒ AWS.Request
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob.
-
listContexts(params = {}, callback) ⇒ AWS.Request
Lists the contexts in your account and their properties.
.
-
listDataQualityJobDefinitions(params = {}, callback) ⇒ AWS.Request
Lists the data quality job definitions in your account.
.
-
listDeviceFleets(params = {}, callback) ⇒ AWS.Request
Returns a list of devices in the fleet.
.
-
listDevices(params = {}, callback) ⇒ AWS.Request
A list of devices.
.
-
listDomains(params = {}, callback) ⇒ AWS.Request
Lists the domains.
.
-
listEdgePackagingJobs(params = {}, callback) ⇒ AWS.Request
Returns a list of edge packaging jobs.
.
-
listEndpointConfigs(params = {}, callback) ⇒ AWS.Request
Lists endpoint configurations.
.
-
listEndpoints(params = {}, callback) ⇒ AWS.Request
Lists endpoints.
.
-
listExperiments(params = {}, callback) ⇒ AWS.Request
Lists all the experiments in your account.
-
listFeatureGroups(params = {}, callback) ⇒ AWS.Request
List
FeatureGroup
s based on given filter and order..
-
listFlowDefinitions(params = {}, callback) ⇒ AWS.Request
Returns information about the flow definitions in your account.
.
-
listHumanTaskUis(params = {}, callback) ⇒ AWS.Request
Returns information about the human task user interfaces in your account.
.
-
listHyperParameterTuningJobs(params = {}, callback) ⇒ AWS.Request
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
.
-
listImages(params = {}, callback) ⇒ AWS.Request
Lists the images in your account and their properties.
-
listImageVersions(params = {}, callback) ⇒ AWS.Request
Lists the versions of a specified image and their properties.
-
listInferenceRecommendationsJobs(params = {}, callback) ⇒ AWS.Request
Lists recommendation jobs that satisfy various filters.
.
-
listLabelingJobs(params = {}, callback) ⇒ AWS.Request
Gets a list of labeling jobs.
.
-
listLabelingJobsForWorkteam(params = {}, callback) ⇒ AWS.Request
Gets a list of labeling jobs assigned to a specified work team.
.
-
listLineageGroups(params = {}, callback) ⇒ AWS.Request
A list of lineage groups shared with your Amazon Web Services account.
-
listModelBiasJobDefinitions(params = {}, callback) ⇒ AWS.Request
Lists model bias jobs definitions that satisfy various filters.
.
-
listModelExplainabilityJobDefinitions(params = {}, callback) ⇒ AWS.Request
Lists model explainability job definitions that satisfy various filters.
.
-
listModelMetadata(params = {}, callback) ⇒ AWS.Request
Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
.
-
listModelPackageGroups(params = {}, callback) ⇒ AWS.Request
Gets a list of the model groups in your Amazon Web Services account.
.
-
listModelPackages(params = {}, callback) ⇒ AWS.Request
Lists the model packages that have been created.
.
-
listModelQualityJobDefinitions(params = {}, callback) ⇒ AWS.Request
Gets a list of model quality monitoring job definitions in your account.
.
-
listModels(params = {}, callback) ⇒ AWS.Request
Lists models created with the
CreateModel
API..
-
listMonitoringExecutions(params = {}, callback) ⇒ AWS.Request
Returns list of all monitoring job executions.
.
-
listMonitoringSchedules(params = {}, callback) ⇒ AWS.Request
Returns list of all monitoring schedules.
.
-
listNotebookInstanceLifecycleConfigs(params = {}, callback) ⇒ AWS.Request
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
.
-
listNotebookInstances(params = {}, callback) ⇒ AWS.Request
Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region.
-
listPipelineExecutions(params = {}, callback) ⇒ AWS.Request
Gets a list of the pipeline executions.
.
-
listPipelineExecutionSteps(params = {}, callback) ⇒ AWS.Request
Gets a list of
PipeLineExecutionStep
objects..
-
listPipelineParametersForExecution(params = {}, callback) ⇒ AWS.Request
Gets a list of parameters for a pipeline execution.
.
-
listPipelines(params = {}, callback) ⇒ AWS.Request
Gets a list of pipelines.
.
-
listProcessingJobs(params = {}, callback) ⇒ AWS.Request
Lists processing jobs that satisfy various filters.
.
-
listProjects(params = {}, callback) ⇒ AWS.Request
Gets a list of the projects in an Amazon Web Services account.
.
-
listStudioLifecycleConfigs(params = {}, callback) ⇒ AWS.Request
Lists the Studio Lifecycle Configurations in your Amazon Web Services Account.
.
-
listSubscribedWorkteams(params = {}, callback) ⇒ AWS.Request
Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace.
-
listTags(params = {}, callback) ⇒ AWS.Request
Returns the tags for the specified SageMaker resource.
.
-
listTrainingJobs(params = {}, callback) ⇒ AWS.Request
Lists training jobs.
Note: WhenStatusEquals
andMaxResults
are set at the same time, theMaxResults
number of training jobs are first retrieved ignoring theStatusEquals
parameter and then they are filtered by theStatusEquals
parameter, which is returned as a response.- listTrainingJobsForHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
.
- listTransformJobs(params = {}, callback) ⇒ AWS.Request
Lists transform jobs.
.
- listTrialComponents(params = {}, callback) ⇒ AWS.Request
Lists the trial components in your account.
- listTrials(params = {}, callback) ⇒ AWS.Request
Lists the trials in your account.
- listUserProfiles(params = {}, callback) ⇒ AWS.Request
Lists user profiles.
.
- listWorkforces(params = {}, callback) ⇒ AWS.Request
Use this operation to list all private and vendor workforces in an Amazon Web Services Region.
- listWorkteams(params = {}, callback) ⇒ AWS.Request
Gets a list of private work teams that you have defined in a region.
- putModelPackageGroupPolicy(params = {}, callback) ⇒ AWS.Request
Adds a resouce policy to control access to a model group.
- queryLineage(params = {}, callback) ⇒ AWS.Request
Use this action to inspect your lineage and discover relationships between entities.
- registerDevices(params = {}, callback) ⇒ AWS.Request
Register devices.
.
- renderUiTemplate(params = {}, callback) ⇒ AWS.Request
Renders the UI template so that you can preview the worker's experience.
- retryPipelineExecution(params = {}, callback) ⇒ AWS.Request
Retry the execution of the pipeline.
.
- search(params = {}, callback) ⇒ AWS.Request
Finds Amazon SageMaker resources that match a search query.
- sendPipelineExecutionStepFailure(params = {}, callback) ⇒ AWS.Request
Notifies the pipeline that the execution of a callback step failed, along with a message describing why.
- sendPipelineExecutionStepSuccess(params = {}, callback) ⇒ AWS.Request
Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters.
- startMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Starts a previously stopped monitoring schedule.
Note: By default, when you successfully create a new schedule, the status of a monitoring schedule isscheduled
.- startNotebookInstance(params = {}, callback) ⇒ AWS.Request
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
- startPipelineExecution(params = {}, callback) ⇒ AWS.Request
Starts a pipeline execution.
.
- stopAutoMLJob(params = {}, callback) ⇒ AWS.Request
A method for forcing the termination of a running job.
.
- stopCompilationJob(params = {}, callback) ⇒ AWS.Request
Stops a model compilation job.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal.
- stopEdgePackagingJob(params = {}, callback) ⇒ AWS.Request
Request to stop an edge packaging job.
.
- stopHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3).
- stopInferenceRecommendationsJob(params = {}, callback) ⇒ AWS.Request
Stops an Inference Recommender job.
.
- stopLabelingJob(params = {}, callback) ⇒ AWS.Request
Stops a running labeling job.
- stopMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Stops a previously started monitoring schedule.
.
- stopNotebookInstance(params = {}, callback) ⇒ AWS.Request
Terminates the ML compute instance.
- stopPipelineExecution(params = {}, callback) ⇒ AWS.Request
Stops a pipeline execution.
Callback Step
A pipeline execution won't stop while a callback step is running.
- stopProcessingJob(params = {}, callback) ⇒ AWS.Request
Stops a processing job.
.
- stopTrainingJob(params = {}, callback) ⇒ AWS.Request
Stops a training job.
- stopTransformJob(params = {}, callback) ⇒ AWS.Request
Stops a batch transform job.
When Amazon SageMaker receives a
StopTransformJob
request, the status of the job changes toStopping
.- updateAction(params = {}, callback) ⇒ AWS.Request
Updates an action.
.
- updateAppImageConfig(params = {}, callback) ⇒ AWS.Request
Updates the properties of an AppImageConfig.
.
- updateArtifact(params = {}, callback) ⇒ AWS.Request
Updates an artifact.
.
- updateCodeRepository(params = {}, callback) ⇒ AWS.Request
Updates the specified Git repository with the specified values.
.
- updateContext(params = {}, callback) ⇒ AWS.Request
Updates a context.
.
- updateDeviceFleet(params = {}, callback) ⇒ AWS.Request
Updates a fleet of devices.
.
- updateDevices(params = {}, callback) ⇒ AWS.Request
Updates one or more devices in a fleet.
.
- updateDomain(params = {}, callback) ⇒ AWS.Request
Updates the default settings for new user profiles in the domain.
.
- updateEndpoint(params = {}, callback) ⇒ AWS.Request
Deploys the new
EndpointConfig
specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previousEndpointConfig
(there is no availability loss).- updateEndpointWeightsAndCapacities(params = {}, callback) ⇒ AWS.Request
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint.
- updateExperiment(params = {}, callback) ⇒ AWS.Request
Adds, updates, or removes the description of an experiment.
- updateImage(params = {}, callback) ⇒ AWS.Request
Updates the properties of a SageMaker image.
- updateModelPackage(params = {}, callback) ⇒ AWS.Request
Updates a versioned model.
.
- updateMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Updates a previously created schedule.
.
- updateNotebookInstance(params = {}, callback) ⇒ AWS.Request
Updates a notebook instance.
- updateNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
.
- updatePipeline(params = {}, callback) ⇒ AWS.Request
Updates a pipeline.
.
- updatePipelineExecution(params = {}, callback) ⇒ AWS.Request
Updates a pipeline execution.
.
- updateProject(params = {}, callback) ⇒ AWS.Request
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.
Note: You must not update a project that is in use.- updateTrainingJob(params = {}, callback) ⇒ AWS.Request
Update a model training job to request a new Debugger profiling configuration.
.
- updateTrial(params = {}, callback) ⇒ AWS.Request
Updates the display name of a trial.
.
- updateTrialComponent(params = {}, callback) ⇒ AWS.Request
Updates one or more properties of a trial component.
.
- updateUserProfile(params = {}, callback) ⇒ AWS.Request
Updates a user profile.
.
- updateWorkforce(params = {}, callback) ⇒ AWS.Request
Use this operation to update your workforce.
- updateWorkteam(params = {}, callback) ⇒ AWS.Request
Updates an existing work team with new member definitions or description.
.
- waitFor(state, params = {}, callback) ⇒ AWS.Request
Waits for a given SageMaker resource.
Methods inherited from AWS.Service
makeRequest, makeUnauthenticatedRequest, setupRequestListeners, defineService
Constructor Details
new AWS.SageMaker(options = {}) ⇒ Object
Constructs a service object. This object has one method for each API operation.
Examples:
Constructing a SageMaker object
var sagemaker = new AWS.SageMaker({apiVersion: '2017-07-24'});
Options Hash (options):
-
params
(map)
—
An optional map of parameters to bind to every request sent by this service object. For more information on bound parameters, see "Working with Services" in the Getting Started Guide.
-
endpoint
(String|AWS.Endpoint)
—
The endpoint URI to send requests to. The default endpoint is built from the configured
region
. The endpoint should be a string like'https://{service}.{region}.amazonaws.com'
or an Endpoint object. -
accessKeyId
(String)
—
your AWS access key ID.
-
secretAccessKey
(String)
—
your AWS secret access key.
-
sessionToken
(AWS.Credentials)
—
the optional AWS session token to sign requests with.
-
credentials
(AWS.Credentials)
—
the AWS credentials to sign requests with. You can either specify this object, or specify the accessKeyId and secretAccessKey options directly.
-
credentialProvider
(AWS.CredentialProviderChain)
—
the provider chain used to resolve credentials if no static
credentials
property is set. -
region
(String)
—
the region to send service requests to. See AWS.SageMaker.region for more information.
-
maxRetries
(Integer)
—
the maximum amount of retries to attempt with a request. See AWS.SageMaker.maxRetries for more information.
-
maxRedirects
(Integer)
—
the maximum amount of redirects to follow with a request. See AWS.SageMaker.maxRedirects for more information.
-
sslEnabled
(Boolean)
—
whether to enable SSL for requests.
-
paramValidation
(Boolean|map)
—
whether input parameters should be validated against the operation description before sending the request. Defaults to true. Pass a map to enable any of the following specific validation features:
- min [Boolean] — Validates that a value meets the min
constraint. This is enabled by default when paramValidation is set
to
true
. - max [Boolean] — Validates that a value meets the max constraint.
- pattern [Boolean] — Validates that a string value matches a regular expression.
- enum [Boolean] — Validates that a string value matches one of the allowable enum values.
- min [Boolean] — Validates that a value meets the min
constraint. This is enabled by default when paramValidation is set
to
-
computeChecksums
(Boolean)
—
whether to compute checksums for payload bodies when the service accepts it (currently supported in S3 only)
-
convertResponseTypes
(Boolean)
—
whether types are converted when parsing response data. Currently only supported for JSON based services. Turning this off may improve performance on large response payloads. Defaults to
true
. -
correctClockSkew
(Boolean)
—
whether to apply a clock skew correction and retry requests that fail because of an skewed client clock. Defaults to
false
. -
s3ForcePathStyle
(Boolean)
—
whether to force path style URLs for S3 objects.
-
s3BucketEndpoint
(Boolean)
—
whether the provided endpoint addresses an individual bucket (false if it addresses the root API endpoint). Note that setting this configuration option requires an
endpoint
to be provided explicitly to the service constructor. -
s3DisableBodySigning
(Boolean)
—
whether S3 body signing should be disabled when using signature version
v4
. Body signing can only be disabled when using https. Defaults totrue
. -
s3UsEast1RegionalEndpoint
('legacy'|'regional')
—
when region is set to 'us-east-1', whether to send s3 request to global endpoints or 'us-east-1' regional endpoints. This config is only applicable to S3 client. Defaults to
legacy
-
s3UseArnRegion
(Boolean)
—
whether to override the request region with the region inferred from requested resource's ARN. Only available for S3 buckets Defaults to
true
-
retryDelayOptions
(map)
—
A set of options to configure the retry delay on retryable errors. Currently supported options are:
- base [Integer] — The base number of milliseconds to use in the exponential backoff for operation retries. Defaults to 100 ms for all services except DynamoDB, where it defaults to 50ms.
- customBackoff [function] — A custom function that accepts a
retry count and error and returns the amount of time to delay in
milliseconds. If the result is a non-zero negative value, no further
retry attempts will be made. The
base
option will be ignored if this option is supplied. The function is only called for retryable errors.
-
httpOptions
(map)
—
A set of options to pass to the low-level HTTP request. Currently supported options are:
- proxy [String] — the URL to proxy requests through
- agent [http.Agent, https.Agent] — the Agent object to perform
HTTP requests with. Used for connection pooling. Defaults to the global
agent (
http.globalAgent
) for non-SSL connections. Note that for SSL connections, a special Agent object is used in order to enable peer certificate verification. This feature is only available in the Node.js environment. - connectTimeout [Integer] — Sets the socket to timeout after
failing to establish a connection with the server after
connectTimeout
milliseconds. This timeout has no effect once a socket connection has been established. - timeout [Integer] — Sets the socket to timeout after timeout milliseconds of inactivity on the socket. Defaults to two minutes (120000).
- xhrAsync [Boolean] — Whether the SDK will send asynchronous HTTP requests. Used in the browser environment only. Set to false to send requests synchronously. Defaults to true (async on).
- xhrWithCredentials [Boolean] — Sets the "withCredentials" property of an XMLHttpRequest object. Used in the browser environment only. Defaults to false.
-
apiVersion
(String, Date)
—
a String in YYYY-MM-DD format (or a date) that represents the latest possible API version that can be used in all services (unless overridden by
apiVersions
). Specify 'latest' to use the latest possible version. -
apiVersions
(map<String, String|Date>)
—
a map of service identifiers (the lowercase service class name) with the API version to use when instantiating a service. Specify 'latest' for each individual that can use the latest available version.
-
logger
(#write, #log)
—
an object that responds to .write() (like a stream) or .log() (like the console object) in order to log information about requests
-
systemClockOffset
(Number)
—
an offset value in milliseconds to apply to all signing times. Use this to compensate for clock skew when your system may be out of sync with the service time. Note that this configuration option can only be applied to the global
AWS.config
object and cannot be overridden in service-specific configuration. Defaults to 0 milliseconds. -
signatureVersion
(String)
—
the signature version to sign requests with (overriding the API configuration). Possible values are: 'v2', 'v3', 'v4'.
-
signatureCache
(Boolean)
—
whether the signature to sign requests with (overriding the API configuration) is cached. Only applies to the signature version 'v4'. Defaults to
true
. -
dynamoDbCrc32
(Boolean)
—
whether to validate the CRC32 checksum of HTTP response bodies returned by DynamoDB. Default:
true
. -
useAccelerateEndpoint
(Boolean)
—
Whether to use the S3 Transfer Acceleration endpoint with the S3 service. Default:
false
. -
clientSideMonitoring
(Boolean)
—
whether to collect and publish this client's performance metrics of all its API requests.
-
endpointDiscoveryEnabled
(Boolean|undefined)
—
whether to call operations with endpoints given by service dynamically. Setting this
-
endpointCacheSize
(Number)
—
the size of the global cache storing endpoints from endpoint discovery operations. Once endpoint cache is created, updating this setting cannot change existing cache size. Defaults to 1000
-
hostPrefixEnabled
(Boolean)
—
whether to marshal request parameters to the prefix of hostname. Defaults to
true
. -
stsRegionalEndpoints
('legacy'|'regional')
—
whether to send sts request to global endpoints or regional endpoints. Defaults to 'legacy'.
-
useFipsEndpoint
(Boolean)
—
Enables FIPS compatible endpoints. Defaults to
false
. -
useDualstackEndpoint
(Boolean)
—
Enables IPv6 dualstack endpoint. Defaults to
false
.
Property Details
Method Details
addAssociation(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the addAssociation operation
var params = { DestinationArn: 'STRING_VALUE', /* required */ SourceArn: 'STRING_VALUE', /* required */ AssociationType: ContributedTo | AssociatedWith | DerivedFrom | Produced }; sagemaker.addAssociation(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SourceArn
— (String
)The ARN of the source.
DestinationArn
— (String
)The Amazon Resource Name (ARN) of the destination.
AssociationType
— (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.
"ContributedTo"
"AssociatedWith"
"DerivedFrom"
"Produced"
-
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:SourceArn
— (String
)The ARN of the source.
DestinationArn
— (String
)The Amazon Resource Name (ARN) of the destination.
-
(AWS.Response)
—
Returns:
addTags(params = {}, callback) ⇒ AWS.Request
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.
Note: 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 theTags
parameter of CreateHyperParameterTuningJobNote: Tags that you add to a SageMaker Studio Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in theTags
parameter of CreateDomain or CreateUserProfile.Service Reference:
Examples:
Calling the addTags operation
var params = { ResourceArn: 'STRING_VALUE', /* required */ Tags: [ /* required */ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.addTags(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ResourceArn
— (String
)The Amazon Resource Name (ARN) of the resource that you want to tag.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Tags
— (Array<map>
)A list of tags associated with the SageMaker resource.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
-
(AWS.Response)
—
Returns:
associateTrialComponent(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the associateTrialComponent operation
var params = { TrialComponentName: 'STRING_VALUE', /* required */ TrialName: 'STRING_VALUE' /* required */ }; sagemaker.associateTrialComponent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrialComponentName
— (String
)The name of the component to associated with the trial.
TrialName
— (String
)The name of the trial to associate with.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialComponentArn
— (String
)The ARN of the trial component.
TrialArn
— (String
)The Amazon Resource Name (ARN) of the trial.
-
(AWS.Response)
—
Returns:
batchDescribeModelPackage(params = {}, callback) ⇒ AWS.Request
This action batch describes a list of versioned model packages
Service Reference:
Examples:
Calling the batchDescribeModelPackage operation
var params = { ModelPackageArnList: [ /* required */ 'STRING_VALUE', /* more items */ ] }; sagemaker.batchDescribeModelPackage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageArnList
— (Array<String>
)The list of Amazon Resource Name (ARN) of the model package groups.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageSummaries
— (map<map>
)The summaries for the model package versions
ModelPackageGroupName
— required — (String
)The group name for the model package
ModelPackageVersion
— (Integer
)The version number of a versioned model.
ModelPackageArn
— required — (String
)The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription
— (String
)The description of the model package.
CreationTime
— required — (Date
)The creation time of the mortgage package summary.
InferenceSpecification
— required — (map
)Defines how to perform inference generation after a training job is run.
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes
— required — (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— required — (Array<String>
)The supported MIME types for the output data.
ModelPackageStatus
— required — (String
)The status of the mortgage package.
Possible values include:"Pending"
"InProgress"
"Completed"
"Failed"
"Deleting"
ModelApprovalStatus
— (String
)The approval status of the model.
Possible values include:"Approved"
"Rejected"
"PendingManualApproval"
BatchDescribeModelPackageErrorMap
— (map<map>
)A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.
ErrorCode
— required — (String
)ErrorResponse
— required — (String
)
-
(AWS.Response)
—
Returns:
createAction(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the createAction operation
var params = { ActionName: 'STRING_VALUE', /* required */ ActionType: 'STRING_VALUE', /* required */ Source: { /* required */ SourceUri: 'STRING_VALUE', /* required */ SourceId: 'STRING_VALUE', SourceType: 'STRING_VALUE' }, Description: 'STRING_VALUE', MetadataProperties: { CommitId: 'STRING_VALUE', GeneratedBy: 'STRING_VALUE', ProjectId: 'STRING_VALUE', Repository: 'STRING_VALUE' }, Properties: { '<StringParameterValue>': 'STRING_VALUE', /* '<StringParameterValue>': ... */ }, Status: Unknown | InProgress | Completed | Failed | Stopping | Stopped, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createAction(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ActionName
— (String
)The name of the action. Must be unique to your account in an Amazon Web Services Region.
Source
— (map
)The source type, ID, and URI.
SourceUri
— required — (String
)The URI of the source.
SourceType
— (String
)The type of the source.
SourceId
— (String
)The ID of the source.
ActionType
— (String
)The action type.
Description
— (String
)The description of the action.
Status
— (String
)The status of the action.
Possible values include:"Unknown"
"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
Properties
— (map<String>
)A list of properties to add to the action.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
Tags
— (Array<map>
)A list of tags to apply to the action.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ActionArn
— (String
)The Amazon Resource Name (ARN) of the action.
-
(AWS.Response)
—
Returns:
createAlgorithm(params = {}, callback) ⇒ AWS.Request
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
Service Reference:
Examples:
Calling the createAlgorithm operation
var params = { AlgorithmName: 'STRING_VALUE', /* required */ TrainingSpecification: { /* required */ SupportedTrainingInstanceTypes: [ /* required */ 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, /* more items */ ], TrainingChannels: [ /* required */ { Name: 'STRING_VALUE', /* required */ SupportedContentTypes: [ /* required */ 'STRING_VALUE', /* more items */ ], SupportedInputModes: [ /* required */ Pipe | File | FastFile, /* more items */ ], Description: 'STRING_VALUE', IsRequired: true || false, SupportedCompressionTypes: [ None | Gzip, /* more items */ ] }, /* more items */ ], TrainingImage: 'STRING_VALUE', /* required */ MetricDefinitions: [ { Name: 'STRING_VALUE', /* required */ Regex: 'STRING_VALUE' /* required */ }, /* more items */ ], SupportedHyperParameters: [ { Name: 'STRING_VALUE', /* required */ Type: Integer | Continuous | Categorical | FreeText, /* required */ DefaultValue: 'STRING_VALUE', Description: 'STRING_VALUE', IsRequired: true || false, IsTunable: true || false, Range: { CategoricalParameterRangeSpecification: { Values: [ /* required */ 'STRING_VALUE', /* more items */ ] }, ContinuousParameterRangeSpecification: { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE' /* required */ }, IntegerParameterRangeSpecification: { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE' /* required */ } } }, /* more items */ ], SupportedTuningJobObjectiveMetrics: [ { MetricName: 'STRING_VALUE', /* required */ Type: Maximize | Minimize /* required */ }, /* more items */ ], SupportsDistributedTraining: true || false, TrainingImageDigest: 'STRING_VALUE' }, AlgorithmDescription: 'STRING_VALUE', CertifyForMarketplace: true || false, InferenceSpecification: { Containers: [ /* required */ { Image: 'STRING_VALUE', /* required */ ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Framework: 'STRING_VALUE', FrameworkVersion: 'STRING_VALUE', ImageDigest: 'STRING_VALUE', ModelDataUrl: 'STRING_VALUE', ModelInput: { DataInputConfig: 'STRING_VALUE' /* required */ }, NearestModelName: 'STRING_VALUE', ProductId: 'STRING_VALUE' }, /* more items */ ], SupportedContentTypes: [ /* required */ 'STRING_VALUE', /* more items */ ], SupportedResponseMIMETypes: [ /* required */ 'STRING_VALUE', /* more items */ ], SupportedRealtimeInferenceInstanceTypes: [ 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, /* more items */ ], SupportedTransformInstanceTypes: [ 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, /* more items */ ] }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], ValidationSpecification: { ValidationProfiles: [ /* required */ { ProfileName: 'STRING_VALUE', /* required */ TrainingJobDefinition: { /* required */ InputDataConfig: [ /* required */ { ChannelName: 'STRING_VALUE', /* required */ DataSource: { /* required */ FileSystemDataSource: { DirectoryPath: 'STRING_VALUE', /* required */ FileSystemAccessMode: rw | ro, /* required */ FileSystemId: 'STRING_VALUE', /* required */ FileSystemType: EFS | FSxLustre /* required */ }, S3DataSource: { S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE', /* required */ AttributeNames: [ 'STRING_VALUE', /* more items */ ], S3DataDistributionType: FullyReplicated | ShardedByS3Key } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', InputMode: Pipe | File | FastFile, RecordWrapperType: None | RecordIO, ShuffleConfig: { Seed: 'NUMBER_VALUE' /* required */ } }, /* more items */ ], OutputDataConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, ResourceConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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, /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' }, StoppingCondition: { /* required */ MaxRuntimeInSeconds: 'NUMBER_VALUE', MaxWaitTimeInSeconds: 'NUMBER_VALUE' }, TrainingInputMode: Pipe | File | FastFile, /* required */ HyperParameters: { '<HyperParameterKey>': 'STRING_VALUE', /* '<HyperParameterKey>': ... */ } }, TransformJobDefinition: { TransformInput: { /* required */ DataSource: { /* required */ S3DataSource: { /* required */ S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE' /* required */ } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', SplitType: None | Line | RecordIO | TFRecord }, TransformOutput: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ Accept: 'STRING_VALUE', AssembleWith: None | Line, KmsKeyId: 'STRING_VALUE' }, TransformResources: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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_VALUE' }, BatchStrategy: MultiRecord | SingleRecord, Environment: { '<TransformEnvironmentKey>': 'STRING_VALUE', /* '<TransformEnvironmentKey>': ... */ }, MaxConcurrentTransforms: 'NUMBER_VALUE', MaxPayloadInMB: 'NUMBER_VALUE' } }, /* more items */ ], ValidationRole: 'STRING_VALUE' /* required */ } }; sagemaker.createAlgorithm(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AlgorithmName
— (String
)The name of the algorithm.
AlgorithmDescription
— (String
)A description of the algorithm.
TrainingSpecification
— (map
)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
, andtest
channels.
TrainingImage
— required — (String
)The Amazon ECR registry path of the Docker image that contains the training algorithm.
TrainingImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
SupportedHyperParameters
— (Array<map>
)A list of the
HyperParameterSpecification
objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>Name
— required — (String
)The name of this hyperparameter. The name must be unique.
Description
— (String
)A brief description of the hyperparameter.
Type
— required — (String
)The type of this hyperparameter. The valid types are
Possible values include:Integer
,Continuous
,Categorical
, andFreeText
."Integer"
"Continuous"
"Categorical"
"FreeText"
Range
— (map
)The allowed range for this hyperparameter.
IntegerParameterRangeSpecification
— (map
)A
IntegerParameterRangeSpecification
object that defines the possible values for an integer hyperparameter.MinValue
— required — (String
)The minimum integer value allowed.
MaxValue
— required — (String
)The maximum integer value allowed.
ContinuousParameterRangeSpecification
— (map
)A
ContinuousParameterRangeSpecification
object that defines the possible values for a continuous hyperparameter.MinValue
— required — (String
)The minimum floating-point value allowed.
MaxValue
— required — (String
)The maximum floating-point value allowed.
CategoricalParameterRangeSpecification
— (map
)A
CategoricalParameterRangeSpecification
object that defines the possible values for a categorical hyperparameter.Values
— required — (Array<String>
)The allowed categories for the hyperparameter.
IsTunable
— (Boolean
)Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
IsRequired
— (Boolean
)Indicates whether this hyperparameter is required.
DefaultValue
— (String
)The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
SupportedTrainingInstanceTypes
— required — (Array<String>
)A list of the instance types that this algorithm can use for training.
SupportsDistributedTraining
— (Boolean
)Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
MetricDefinitions
— (Array<map>
)A list of
MetricDefinition
objects, which are used for parsing metrics generated by the algorithm.Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics.
TrainingChannels
— required — (Array<map>
)A list of
ChannelSpecification
objects, which specify the input sources to be used by the algorithm.Name
— required — (String
)The name of the channel.
Description
— (String
)A brief description of the channel.
IsRequired
— (Boolean
)Indicates whether the channel is required by the algorithm.
SupportedContentTypes
— required — (Array<String>
)The supported MIME types for the data.
SupportedCompressionTypes
— (Array<String>
)The allowed compression types, if data compression is used.
SupportedInputModes
— required — (Array<String>
)The allowed input mode, either FILE or PIPE.
In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.
In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
SupportedTuningJobObjectiveMetrics
— (Array<map>
)A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
-
InferenceSpecification
— (map
)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.
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes
— required — (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— required — (Array<String>
)The supported MIME types for the output data.
-
ValidationSpecification
— (map
)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.
ValidationRole
— required — (String
)The IAM roles that SageMaker uses to run the training jobs.
ValidationProfiles
— required — (Array<map>
)An array of
AlgorithmValidationProfile
objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.ProfileName
— required — (String
)The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
TrainingJobDefinition
— required — (map
)The
TrainingJobDefinition
object that describes the training job that SageMaker runs to validate your algorithm.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
HyperParameters
— (map<String>
)The hyperparameters used for the training job.
InputDataConfig
— required — (Array<map>
)An array of
Channel
objects, each of which specifies an input source.ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
OutputDataConfig
— required — (map
)the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys forOutputDataConfig
. If you use a bucket policy with ans3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.The KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.
ResourceConfig
— required — (map
)The resources, including the ML compute instances and ML storage volumes, to use for model training.
InstanceType
— required — (String
)The ML compute instance type.
Possible values include:"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"
InstanceCount
— required — (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.You must specify sufficient ML storage for your scenario.
Note: SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.Note: Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request aVolumeSizeInGB
greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
StoppingCondition
— required — (map
)Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.
TransformJobDefinition
— (map
)The
TransformJobDefinition
object that describes the transform job that SageMaker runs to validate your algorithm.MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB
— (Integer
)The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy
— (String
)A string that determines the number of records included in a single mini-batch.
SingleRecord
means only one record is used per mini-batch.MultiRecord
means a mini-batch is set to contain as many records that can fit within theMaxPayloadInMB
limit."MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— required — (map
)A description of the input source and the way the transform job consumes it.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— required — (map
)Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
TransformResources
— required — (map
)Identifies the ML compute instances for the transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."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"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is
1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
CertifyForMarketplace
— (Boolean
)Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AlgorithmArn
— (String
)The Amazon Resource Name (ARN) of the new algorithm.
-
(AWS.Response)
—
Returns:
createApp(params = {}, callback) ⇒ AWS.Request
Creates a running app for the specified UserProfile. Supported apps are
JupyterServer
andKernelGateway
. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.Service Reference:
Examples:
Calling the createApp operation
var params = { AppName: 'STRING_VALUE', /* required */ AppType: JupyterServer | KernelGateway | TensorBoard | RStudioServerPro | RSessionGateway, /* required */ DomainId: 'STRING_VALUE', /* required */ UserProfileName: 'STRING_VALUE', /* 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, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createApp(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The user profile name.
AppType
— (String
)The type of app. Supported apps are
Possible values include:JupyterServer
andKernelGateway
.TensorBoard
is not supported."JupyterServer"
"KernelGateway"
"TensorBoard"
"RStudioServerPro"
"RSessionGateway"
AppName
— (String
)The name of the app.
Tags
— (Array<map>
)Each tag consists of a key and an optional value. Tag keys must be unique per resource.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ResourceSpec
— (map
)The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
Note: The value ofInstanceType
passed as part of theResourceSpec
in theCreateApp
call overrides the value passed as part of theResourceSpec
configured for the user profile or the domain. IfInstanceType
is not specified in any of those threeResourceSpec
values for aKernelGateway
app, theCreateApp
call fails with a request validation error.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."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"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AppArn
— (String
)The Amazon Resource Name (ARN) of the app.
-
(AWS.Response)
—
Returns:
createAppImageConfig(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the createAppImageConfig operation
var params = { AppImageConfigName: 'STRING_VALUE', /* required */ KernelGatewayImageConfig: { KernelSpecs: [ /* required */ { Name: 'STRING_VALUE', /* required */ DisplayName: 'STRING_VALUE' }, /* more items */ ], FileSystemConfig: { DefaultGid: 'NUMBER_VALUE', DefaultUid: 'NUMBER_VALUE', MountPath: 'STRING_VALUE' } }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createAppImageConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AppImageConfigName
— (String
)The name of the AppImageConfig. Must be unique to your account.
Tags
— (Array<map>
)A list of tags to apply to the AppImageConfig.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
KernelGatewayImageConfig
— (map
)The KernelGatewayImageConfig.
KernelSpecs
— required — (Array<map>
)The specification of the Jupyter kernels in the image.
Name
— required — (String
)The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName
— (String
)The display name of the kernel.
FileSystemConfig
— (map
)The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AppImageConfigArn
— (String
)The Amazon Resource Name (ARN) of the AppImageConfig.
-
(AWS.Response)
—
Returns:
createArtifact(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the createArtifact operation
var params = { ArtifactType: 'STRING_VALUE', /* required */ Source: { /* required */ SourceUri: 'STRING_VALUE', /* required */ SourceTypes: [ { SourceIdType: MD5Hash | S3ETag | S3Version | Custom, /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }, ArtifactName: 'STRING_VALUE', MetadataProperties: { CommitId: 'STRING_VALUE', GeneratedBy: 'STRING_VALUE', ProjectId: 'STRING_VALUE', Repository: 'STRING_VALUE' }, Properties: { '<StringParameterValue>': 'STRING_VALUE', /* '<StringParameterValue>': ... */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createArtifact(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ArtifactName
— (String
)The name of the artifact. Must be unique to your account in an Amazon Web Services Region.
Source
— (map
)The ID, ID type, and URI of the source.
SourceUri
— required — (String
)The URI of the source.
SourceTypes
— (Array<map>
)A list of source types.
SourceIdType
— required — (String
)The type of ID.
Possible values include:"MD5Hash"
"S3ETag"
"S3Version"
"Custom"
Value
— required — (String
)The ID.
ArtifactType
— (String
)The artifact type.
Properties
— (map<String>
)A list of properties to add to the artifact.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
Tags
— (Array<map>
)A list of tags to apply to the artifact.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ArtifactArn
— (String
)The Amazon Resource Name (ARN) of the artifact.
-
(AWS.Response)
—
Returns:
createAutoMLJob(params = {}, callback) ⇒ AWS.Request
Creates an Autopilot job.
Find the best-performing model after you run an Autopilot job by calling .
For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
Service Reference:
Examples:
Calling the createAutoMLJob operation
var params = { AutoMLJobName: 'STRING_VALUE', /* required */ InputDataConfig: [ /* required */ { DataSource: { /* required */ S3DataSource: { /* required */ S3DataType: ManifestFile | S3Prefix, /* required */ S3Uri: 'STRING_VALUE' /* required */ } }, TargetAttributeName: 'STRING_VALUE', /* required */ ChannelType: training | validation, CompressionType: None | Gzip, ContentType: 'STRING_VALUE' }, /* more items */ ], OutputDataConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ AutoMLJobConfig: { CandidateGenerationConfig: { FeatureSpecificationS3Uri: 'STRING_VALUE' }, CompletionCriteria: { MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE', MaxCandidates: 'NUMBER_VALUE', MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE' }, DataSplitConfig: { ValidationFraction: 'NUMBER_VALUE' }, SecurityConfig: { EnableInterContainerTrafficEncryption: true || false, VolumeKmsKeyId: 'STRING_VALUE', VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } } }, AutoMLJobObjective: { MetricName: Accuracy | MSE | F1 | F1macro | AUC /* required */ }, GenerateCandidateDefinitionsOnly: true || false, ModelDeployConfig: { AutoGenerateEndpointName: true || false, EndpointName: 'STRING_VALUE' }, ProblemType: BinaryClassification | MulticlassClassification | Regression, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createAutoMLJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AutoMLJobName
— (String
)Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.
InputDataConfig
— (Array<map>
)An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to
InputDataConfig
supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.DataSource
— required — (map
)The data source for an AutoML channel.
S3DataSource
— required — (map
)The Amazon S3 location of the input data.
S3DataType
— required — (String
)The data type.
A ManifestFile should have the format shown below:
[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",
... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
An S3Prefix should have the following format:
s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
"ManifestFile"
"S3Prefix"
S3Uri
— required — (String
)The URL to the Amazon S3 data source.
CompressionType
— (String
)You can use
Possible values include:Gzip
orNone
. The default value isNone
."None"
"Gzip"
TargetAttributeName
— required — (String
)The name of the target variable in supervised learning, usually represented by 'y'.
ContentType
— (String
)The content type of the data from the input source. You can use
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
.ChannelType
— (String
)The channel type (optional) is an
Possible values include:enum
string. The default value istraining
. Channels for training and validation must share the sameContentType
andTargetAttributeName
. For information on specifying training and validation channel types, seeHow to specify training and validation datasets
."training"
"validation"
OutputDataConfig
— (map
)Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
KmsKeyId
— (String
)The Amazon Web Services KMS encryption key ID.
S3OutputPath
— required — (String
)The Amazon S3 output path. Must be 128 characters or less.
ProblemType
— (String
)Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.
Possible values include:"BinaryClassification"
"MulticlassClassification"
"Regression"
AutoMLJobObjective
— (map
)Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.
MetricName
— required — (String
)The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
-
MSE
: The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive: the better a model is at predicting the actual values, the smaller the MSE value is. When the data contains outliers, they tend to dominate the MSE, which might cause subpar prediction performance. -
Accuracy
: The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for binary and multiclass classification. It measures how close the predicted class values are to the actual values. Accuracy values vary between zero and one: one indicates perfect accuracy and zero indicates perfect inaccuracy. -
F1
: The F1 score is the harmonic mean of the precision and recall. It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class and false when they do not. Precision is the ratio of the true positive predictions to all positive predictions (including the false positives) in a data set and measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances and measures how completely a model predicts the actual class members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary between zero and one: one indicates the best possible performance and zero the worst. -
AUC
: The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities into classifications. The relevant curve is the receiver operating characteristic curve that plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so provides an aggregated measure of the model performance across all possible classification thresholds. The AUC score can also be interpreted as the probability that a randomly selected positive data point is more likely to be predicted positive than a randomly selected negative example. AUC scores vary between zero and one: a score of one indicates perfect accuracy and a score of one half indicates that the prediction is not better than a random classifier. Values under one half predict less accurately than a random predictor. But such consistently bad predictors can simply be inverted to obtain better than random predictors. -
F1macro
: The F1macro score applies F1 scoring to multiclass classification. In this context, you have multiple classes to predict. You just calculate the precision and recall for each class as you did for the positive class in binary classification. Then, use these values to calculate the F1 score for each class and average them to obtain the F1macro score. F1macro scores vary between zero and one: one indicates the best possible performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
-
MSE
: for regression. -
F1
: for binary classification -
Accuracy
: for multiclass classification.
"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
-
AutoMLJobConfig
— (map
)A collection of settings used to configure an AutoML job.
CompletionCriteria
— (map
)How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job is allowed to run as part of a hyperparameter tuning job. For more information, see the used by the action.
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, will not be completed.
SecurityConfig
— (map
)The security configuration for traffic encryption or Amazon VPC settings.
VolumeKmsKeyId
— (String
)The key used to encrypt stored data.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to use traffic encryption between the container layers.
VpcConfig
— (map
)The VPC configuration.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
DataSplitConfig
— (map
)The configuration for splitting the input training dataset.
Type: AutoMLDataSplitConfig
ValidationFraction
— (Float
)The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
CandidateGenerationConfig
— (map
)The configuration for generating a candidate for an AutoML job (optional).
FeatureSpecificationS3Uri
— (String
)A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job (optional). This file should be in json format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] }
.The key name
FeatureAttributeNames
is fixed. The values listed in["col1", "col2", ...]
is case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.
RoleArn
— (String
)The ARN of the role that is used to access the data.
GenerateCandidateDefinitionsOnly
— (Boolean
)Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
Tags
— (Array<map>
)Each tag consists of a key and an optional value. Tag keys must be unique per resource.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ModelDeployConfig
— (map
)Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
AutoGenerateEndpointName
— (Boolean
)Set to
True
to automatically generate an endpoint name for a one-click Autopilot model deployment; set toFalse
otherwise. The default value isFalse
.Note: If you setAutoGenerateEndpointName
toTrue
, do not specify theEndpointName
; otherwise a 400 error is thrown.EndpointName
— (String
)Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Note: Specify theEndpointName
if and only if you setAutoGenerateEndpointName
toFalse
; otherwise a 400 error is thrown.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AutoMLJobArn
— (String
)The unique ARN assigned to the AutoML job when it is created.
-
(AWS.Response)
—
Returns:
createCodeRepository(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the createCodeRepository operation
var params = { CodeRepositoryName: 'STRING_VALUE', /* required */ GitConfig: { /* required */ RepositoryUrl: 'STRING_VALUE', /* required */ Branch: 'STRING_VALUE', SecretArn: 'STRING_VALUE' }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createCodeRepository(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CodeRepositoryName
— (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
— (map
)Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.
RepositoryUrl
— required — (String
)The URL where the Git repository is located.
Branch
— (String
)The default branch for the Git repository.
SecretArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of
AWSCURRENT
and must be in the following format:{"username": UserName, "password": Password}
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CodeRepositoryArn
— (String
)The Amazon Resource Name (ARN) of the new repository.
-
(AWS.Response)
—
Returns:
createCompilationJob(params = {}, callback) ⇒ AWS.Request
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 theCompilationJobArn
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.
Service Reference:
Examples:
Calling the createCompilationJob operation
var params = { CompilationJobName: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputLocation: 'STRING_VALUE', /* required */ CompilerOptions: 'STRING_VALUE', KmsKeyId: 'STRING_VALUE', TargetDevice: lambda | ml_m4 | ml_m5 | ml_c4 | ml_c5 | ml_p2 | ml_p3 | ml_g4dn | ml_inf1 | 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: { Arch: X86_64 | X86 | ARM64 | ARM_EABI | ARM_EABIHF, /* required */ Os: ANDROID | LINUX, /* required */ Accelerator: INTEL_GRAPHICS | MALI | NVIDIA | NNA } }, RoleArn: 'STRING_VALUE', /* required */ StoppingCondition: { /* required */ MaxRuntimeInSeconds: 'NUMBER_VALUE', MaxWaitTimeInSeconds: 'NUMBER_VALUE' }, InputConfig: { DataInputConfig: 'STRING_VALUE', /* required */ Framework: TENSORFLOW | KERAS | MXNET | ONNX | PYTORCH | XGBOOST | TFLITE | DARKNET | SKLEARN, /* required */ S3Uri: 'STRING_VALUE', /* required */ FrameworkVersion: 'STRING_VALUE' }, ModelPackageVersionArn: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }; sagemaker.createCompilationJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CompilationJobName
— (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.
RoleArn
— (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:PassRole
permission. For more information, see Amazon SageMaker Roles.-
ModelPackageVersionArn
— (String
)The Amazon Resource Name (ARN) of a versioned model package. Provide either a
ModelPackageVersionArn
or anInputConfig
object in the request syntax. The presence of both objects in theCreateCompilationJob
request will return an exception.InputConfig
— (map
)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.
S3Uri
— required — (String
)The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
DataInputConfig
— required — (String
)Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.
-
TensorFlow
: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"input":[1,1024,1024,3]}
-
If using the CLI,
{\"input\":[1,1024,1024,3]}
-
-
Examples for two inputs:
-
If using the console,
{"data1": [1,28,28,1], "data2":[1,28,28,1]}
-
If using the CLI,
{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
-
-
-
KERAS
: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format,DataInputConfig
should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"input_1":[1,3,224,224]}
-
If using the CLI,
{\"input_1\":[1,3,224,224]}
-
-
Examples for two inputs:
-
If using the console,
{"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
-
If using the CLI,
{\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
-
-
-
MXNET/ONNX/DARKNET
: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"data":[1,3,1024,1024]}
-
If using the CLI,
{\"data\":[1,3,1024,1024]}
-
-
Examples for two inputs:
-
If using the console,
{"var1": [1,1,28,28], "var2":[1,1,28,28]}
-
If using the CLI,
{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
-
-
-
PyTorch
: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.-
Examples for one input in dictionary format:
-
If using the console,
{"input0":[1,3,224,224]}
-
If using the CLI,
{\"input0\":[1,3,224,224]}
-
-
Example for one input in list format:
[[1,3,224,224]]
-
Examples for two inputs in dictionary format:
-
If using the console,
{"input0":[1,3,224,224], "input1":[1,3,224,224]}
-
If using the CLI,
{\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
-
-
Example for two inputs in list format:
[[1,3,224,224], [1,3,224,224]]
-
-
XGBOOST
: input data name and shape are not needed.
DataInputConfig
supports the following parameters forCoreML
OutputConfig$TargetDevice (ML Model format):-
shape
: Input shape, for example{"input_1": {"shape": [1,224,224,3]}}
. In addition to static input shapes, CoreML converter supports Flexible input shapes:-
Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example:
{"input_1": {"shape": ["1..10", 224, 224, 3]}}
-
Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example:
{"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
-
-
default_shape
: Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example{"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
-
type
: Input type. Allowed values:Image
andTensor
. By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such asbias
andscale
. -
bias
: If the input type is an Image, you need to provide the bias vector. -
scale
: If the input type is an Image, you need to provide a scale factor.
CoreML
ClassifierConfig
parameters can be specified using OutputConfig$CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:-
Tensor type input:
-
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
-
-
Tensor type input without input name (PyTorch):
-
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
-
-
Image type input:
-
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
-
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
-
-
Image type input without input name (PyTorch):
-
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
-
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
-
Depending on the model format,
DataInputConfig
requires the following parameters forml_eia2
OutputConfig:TargetDevice.-
For TensorFlow models saved in the SavedModel format, specify the input names from
signature_def_key
and the input model shapes forDataInputConfig
. Specify thesignature_def_key
inOutputConfig:CompilerOptions
if the model does not use TensorFlow's default signature def key. For example:-
"DataInputConfig": {"inputs": [1, 224, 224, 3]}
-
"CompilerOptions": {"signature_def_key": "serving_custom"}
-
-
For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
DataInputConfig
and the output tensor names foroutput_names
inOutputConfig:CompilerOptions
. For example:-
"DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
-
"CompilerOptions": {"output_names": ["output_tensor:0"]}
-
-
Framework
— required — (String
)Identifies the framework in which the model was trained. For example: TENSORFLOW.
Possible values include:"TENSORFLOW"
"KERAS"
"MXNET"
"ONNX"
"PYTORCH"
"XGBOOST"
"TFLITE"
"DARKNET"
"SKLEARN"
FrameworkVersion
— (String
)Specifies the framework version to use. This API field is only supported for the PyTorch and TensorFlow frameworks.
For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.
OutputConfig
— (map
)Provides information about the output location for the compiled model and the target device the model runs on.
S3OutputLocation
— required — (String
)Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.TargetDevice
— (String
)Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of
Possible values include:TargetPlatform
."lambda"
"ml_m4"
"ml_m5"
"ml_c4"
"ml_c5"
"ml_p2"
"ml_p3"
"ml_g4dn"
"ml_inf1"
"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
— (map
)Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of
TargetDevice
.The following examples show how to configure the
TargetPlatform
andCompilerOptions
JSON strings for popular target platforms:-
Raspberry Pi 3 Model B+
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
"CompilerOptions": {'mattr': ['+neon']}
-
Jetson TX2
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
-
EC2 m5.2xlarge instance OS
"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'mcpu': 'skylake-avx512'}
-
RK3399
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
-
ARMv7 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
"CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
-
ARMv8 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
"CompilerOptions": {'ANDROID_PLATFORM': 29}
Os
— required — (String
)Specifies a target platform OS.
-
LINUX
: Linux-based operating systems. -
ANDROID
: Android operating systems. Android API level can be specified using theANDROID_PLATFORM
compiler option. For example,"CompilerOptions": {'ANDROID_PLATFORM': 28}
"ANDROID"
"LINUX"
-
Arch
— required — (String
)Specifies a target platform architecture.
-
X86_64
: 64-bit version of the x86 instruction set. -
X86
: 32-bit version of the x86 instruction set. -
ARM64
: ARMv8 64-bit CPU. -
ARM_EABIHF
: ARMv7 32-bit, Hard Float. -
ARM_EABI
: ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.
"X86_64"
"X86"
"ARM64"
"ARM_EABI"
"ARM_EABIHF"
-
Accelerator
— (String
)Specifies a target platform accelerator (optional).
-
NVIDIA
: Nvidia graphics processing unit. It also requiresgpu-code
,trt-ver
,cuda-ver
compiler options -
MALI
: ARM Mali graphics processor -
INTEL_GRAPHICS
: Integrated Intel graphics
"INTEL_GRAPHICS"
"MALI"
"NVIDIA"
"NNA"
-
-
CompilerOptions
— (String
)Specifies additional parameters for compiler options in JSON format. The compiler options are
TargetPlatform
specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specifyCompilerOptions.
-
DTYPE
: Specifies the data type for the input. When compiling forml_*
(except forml_inf
) instances using PyTorch framework, provide the data type (dtype) of the model's input."float32"
is used if"DTYPE"
is not specified. Options for data type are:-
float32: Use either
"float"
or"float32"
. -
int64: Use either
"int64"
or"long"
.
For example,
{"dtype" : "float32"}
. -
-
CPU
: Compilation for CPU supports the following compiler options.-
mcpu
: CPU micro-architecture. For example,{'mcpu': 'skylake-avx512'}
-
mattr
: CPU flags. For example,{'mattr': ['+neon', '+vfpv4']}
-
-
ARM
: Details of ARM CPU compilations.-
NEON
: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.For example, add
{'mattr': ['+neon']}
to the compiler options if compiling for ARM 32-bit platform with the NEON support.
-
-
NVIDIA
: Compilation for NVIDIA GPU supports the following compiler options.-
gpu_code
: Specifies the targeted architecture. -
trt-ver
: Specifies the TensorRT versions in x.y.z. format. -
cuda-ver
: Specifies the CUDA version in x.y format.
For example,
{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
-
-
ANDROID
: Compilation for the Android OS supports the following compiler options:-
ANDROID_PLATFORM
: Specifies the Android API levels. Available levels range from 21 to 29. For example,{'ANDROID_PLATFORM': 28}
. -
mattr
: Add{'mattr': ['+neon']}
to compiler options if compiling for ARM 32-bit platform with NEON support.
-
-
INFERENTIA
: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example,"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""
.For information about supported compiler options, see Neuron Compiler CLI.
-
CoreML
: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:-
class_labels
: Specifies the classification labels file name inside input tar.gz file. For example,{"class_labels": "imagenet_labels_1000.txt"}
. Labels inside the txt file should be separated by newlines.
-
-
EIA
: Compilation for the Elastic Inference Accelerator supports the following compiler options:-
precision_mode
: Specifies the precision of compiled artifacts. Supported values are"FP16"
and"FP32"
. Default is"FP32"
. -
signature_def_key
: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key. -
output_names
: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either:signature_def_key
oroutput_names
.
For example:
{"precision_mode": "FP32", "output_names": ["output:0"]}
-
-
KmsKeyId
— (String
)The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
VpcConfig
— (map
)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.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs. IDs have the form of
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.
StoppingCondition
— (map
)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.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CompilationJobArn
— (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.
-
-
(AWS.Response)
—
Returns:
createContext(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the createContext operation
var params = { ContextName: 'STRING_VALUE', /* required */ ContextType: 'STRING_VALUE', /* required */ Source: { /* required */ SourceUri: 'STRING_VALUE', /* required */ SourceId: 'STRING_VALUE', SourceType: 'STRING_VALUE' }, Description: 'STRING_VALUE', Properties: { '<StringParameterValue>': 'STRING_VALUE', /* '<StringParameterValue>': ... */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createContext(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ContextName
— (String
)The name of the context. Must be unique to your account in an Amazon Web Services Region.
Source
— (map
)The source type, ID, and URI.
SourceUri
— required — (String
)The URI of the source.
SourceType
— (String
)The type of the source.
SourceId
— (String
)The ID of the source.
ContextType
— (String
)The context type.
Description
— (String
)The description of the context.
Properties
— (map<String>
)A list of properties to add to the context.
Tags
— (Array<map>
)A list of tags to apply to the context.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ContextArn
— (String
)The Amazon Resource Name (ARN) of the context.
-
(AWS.Response)
—
Returns:
createDataQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
Service Reference:
Examples:
Calling the createDataQualityJobDefinition operation
var params = { DataQualityAppSpecification: { /* required */ ImageUri: 'STRING_VALUE', /* required */ ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ }, PostAnalyticsProcessorSourceUri: 'STRING_VALUE', RecordPreprocessorSourceUri: 'STRING_VALUE' }, DataQualityJobInput: { /* required */ EndpointInput: { /* required */ EndpointName: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' } }, DataQualityJobOutputConfig: { /* required */ MonitoringOutputs: [ /* required */ { S3Output: { /* required */ LocalPath: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, JobDefinitionName: 'STRING_VALUE', /* required */ JobResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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 */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, RoleArn: 'STRING_VALUE', /* required */ DataQualityBaselineConfig: { BaseliningJobName: 'STRING_VALUE', ConstraintsResource: { S3Uri: 'STRING_VALUE' }, StatisticsResource: { S3Uri: 'STRING_VALUE' } }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createDataQualityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name for the monitoring job definition.
DataQualityBaselineConfig
— (map
)Configures the constraints and baselines for the monitoring job.
BaseliningJobName
— (String
)The name of the job that performs baselining for the data quality monitoring job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
StatisticsResource
— (map
)The statistics resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the statistics resource.
DataQualityAppSpecification
— (map
)Specifies the container that runs the monitoring job.
ImageUri
— required — (String
)The container image that the data quality monitoring job runs.
ContainerEntrypoint
— (Array<String>
)The entrypoint for a container used to run a monitoring job.
ContainerArguments
— (Array<String>
)The arguments to send to the container that the monitoring job runs.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
Environment
— (map<String>
)Sets the environment variables in the container that the monitoring job runs.
DataQualityJobInput
— (map
)A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.
EndpointInput
— required — (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
DataQualityJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"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"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (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(s) that run the model monitoring job.
NetworkConfig
— (map
)Specifies networking configuration for the monitoring job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Tags
— (Array<map>
)(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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the job definition.
-
(AWS.Response)
—
Returns:
createDeviceFleet(params = {}, callback) ⇒ AWS.Request
Creates a device fleet.
Service Reference:
Examples:
Calling the createDeviceFleet operation
var params = { DeviceFleetName: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputLocation: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', PresetDeploymentConfig: 'STRING_VALUE', PresetDeploymentType: GreengrassV2Component }, Description: 'STRING_VALUE', EnableIotRoleAlias: true || false, RoleArn: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createDeviceFleet(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DeviceFleetName
— (String
)The name of the fleet that the device belongs to.
RoleArn
— (String
)The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
Description
— (String
)A description of the fleet.
OutputConfig
— (map
)The output configuration for storing sample data collected by the fleet.
S3OutputLocation
— required — (String
)The Amazon Simple Storage (S3) bucker URI.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
PresetDeploymentType
— (String
)The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
Possible values include:"GreengrassV2Component"
PresetDeploymentConfig
— (String
)The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
-
ComponentName
(optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job. -
ComponentDescription
(optional) - Description of the component. -
ComponentVersion
(optional) - The version of the component.Note: Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a major.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification. -
PlatformOS
(optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux. -
PlatformArchitecture
(optional) - The processor architecture for the platform.Supported architectures Windows include: Windows32_x86, Windows64_x64.
Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
-
Tags
— (Array<map>
)Creates tags for the specified fleet.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
EnableIotRoleAlias
— (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".
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
createDomain(params = {}, callback) ⇒ AWS.Request
Creates a
Domain
used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An Amazon Web Services account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.EFS storage
When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.
VPC configuration
All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the
AppNetworkAccessType
parameter.AppNetworkAccessType
corresponds to the network access type that you choose when you onboard to Studio. The following options are available:-
PublicInternetOnly
- Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value. -
VpcOnly
- All Studio traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a SageMaker Studio app successfully.
For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
Service Reference:
Examples:
Calling the createDomain operation
var params = { AuthMode: SSO | IAM, /* required */ DefaultUserSettings: { /* required */ ExecutionRole: 'STRING_VALUE', JupyterServerAppSettings: { 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, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, KernelGatewayAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], 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, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, RSessionAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], 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, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } }, RStudioServerProAppSettings: { AccessStatus: ENABLED | DISABLED, UserGroup: R_STUDIO_ADMIN | R_STUDIO_USER }, SecurityGroups: [ 'STRING_VALUE', /* more items */ ], SharingSettings: { NotebookOutputOption: Allowed | Disabled, S3KmsKeyId: 'STRING_VALUE', S3OutputPath: 'STRING_VALUE' }, 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, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } } }, DomainName: 'STRING_VALUE', /* required */ SubnetIds: [ /* required */ 'STRING_VALUE', /* more items */ ], VpcId: 'STRING_VALUE', /* required */ AppNetworkAccessType: PublicInternetOnly | VpcOnly, AppSecurityGroupManagement: Service | Customer, DomainSettings: { RStudioServerProDomainSettings: { DomainExecutionRoleArn: 'STRING_VALUE', /* 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, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, RStudioConnectUrl: 'STRING_VALUE', RStudioPackageManagerUrl: 'STRING_VALUE' }, SecurityGroupIds: [ 'STRING_VALUE', /* more items */ ] }, HomeEfsFileSystemKmsKeyId: 'STRING_VALUE', KmsKeyId: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createDomain(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainName
— (String
)A name for the domain.
AuthMode
— (String
)The mode of authentication that members use to access the domain.
Possible values include:"SSO"
"IAM"
DefaultUserSettings
— (map
)The default settings to use to create a user profile when
UserSettings
isn't specified in the call to theCreateUserProfile
API.SecurityGroups
is aggregated when specified in both calls. For all other settings inUserSettings
, the values specified inCreateUserProfile
take precedence over those specified inCreateDomain
.ExecutionRole
— (String
)The execution role for the user.
SecurityGroups
— (Array<String>
)The security groups for the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
Optional when the
CreateDomain.AppNetworkAccessType
parameter is set toPublicInternetOnly
.Required when the
CreateDomain.AppNetworkAccessType
parameter is set toVpcOnly
.Amazon SageMaker adds a security group to allow NFS traffic from SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SharingSettings
— (map
)Specifies options for sharing SageMaker Studio notebooks.
NotebookOutputOption
— (String
)Whether to include the notebook cell output when sharing the notebook. The default is
Possible values include:Disabled
."Allowed"
"Disabled"
S3OutputPath
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon S3 bucket used to store the shared notebook snapshots.S3KmsKeyId
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings
— (map
)The Jupyter server's app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."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"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
KernelGatewayAppSettings
— (map
)The kernel gateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."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"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
TensorBoardAppSettings
— (map
)The TensorBoard app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."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"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings
— (map
)A collection of settings that configure user interaction with the
RStudioServerPro
app.AccessStatus
— (String
)Indicates whether the current user has access to the
Possible values include:RStudioServerPro
app."ENABLED"
"DISABLED"
UserGroup
— (String
)The level of permissions that the user has within the
Possible values include:RStudioServerPro
app. This value defaults toUser
. TheAdmin
value allows the user access to the RStudio Administrative Dashboard."R_STUDIO_ADMIN"
"R_STUDIO_USER"
RSessionAppSettings
— (map
)A collection of settings that configure the
RSessionGateway
app.DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."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"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a RSession app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
SubnetIds
— (Array<String>
)The VPC subnets that Studio uses for communication.
VpcId
— (String
)The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
Tags
— (Array<map>
)Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the
Search
API.Tags that you specify for the Domain are also added to all Apps that the Domain launches.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
AppNetworkAccessType
— (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 Studio traffic is through the specified VPC and subnets
"PublicInternetOnly"
"VpcOnly"
-
HomeEfsFileSystemKmsKeyId
— (String
)Use
KmsKeyId
.KmsKeyId
— (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.
AppSecurityGroupManagement
— (String
)The entity that creates and manages the required security groups for inter-app communication in
Possible values include:VPCOnly
mode. Required whenCreateDomain.AppNetworkAccessType
isVPCOnly
andDomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn
is provided."Service"
"Customer"
DomainSettings
— (map
)A collection of
Domain
settings.SecurityGroupIds
— (Array<String>
)The security groups for the Amazon Virtual Private Cloud that the
Domain
uses for communication between Domain-level apps and user apps.RStudioServerProDomainSettings
— (map
)A collection of settings that configure the
RStudioServerPro
Domain-level app.DomainExecutionRoleArn
— required — (String
)The ARN of the execution role for the
RStudioServerPro
Domain-level app.RStudioConnectUrl
— (String
)A URL pointing to an RStudio Connect server.
RStudioPackageManagerUrl
— (String
)A URL pointing to an RStudio Package Manager server.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."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"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DomainArn
— (String
)The Amazon Resource Name (ARN) of the created domain.
Url
— (String
)The URL to the created domain.
-
(AWS.Response)
—
Returns:
createEdgePackagingJob(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the createEdgePackagingJob operation
var params = { CompilationJobName: 'STRING_VALUE', /* required */ EdgePackagingJobName: 'STRING_VALUE', /* required */ ModelName: 'STRING_VALUE', /* required */ ModelVersion: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputLocation: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', PresetDeploymentConfig: 'STRING_VALUE', PresetDeploymentType: GreengrassV2Component }, RoleArn: 'STRING_VALUE', /* required */ ResourceKey: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createEdgePackagingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EdgePackagingJobName
— (String
)The name of the edge packaging job.
CompilationJobName
— (String
)The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.
ModelName
— (String
)The name of the model.
ModelVersion
— (String
)The version of the model.
RoleArn
— (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.
OutputConfig
— (map
)Provides information about the output location for the packaged model.
S3OutputLocation
— required — (String
)The Amazon Simple Storage (S3) bucker URI.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
PresetDeploymentType
— (String
)The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
Possible values include:"GreengrassV2Component"
PresetDeploymentConfig
— (String
)The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
-
ComponentName
(optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job. -
ComponentDescription
(optional) - Description of the component. -
ComponentVersion
(optional) - The version of the component.Note: Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a major.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification. -
PlatformOS
(optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux. -
PlatformArchitecture
(optional) - The processor architecture for the platform.Supported architectures Windows include: Windows32_x86, Windows64_x64.
Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
-
ResourceKey
— (String
)The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.
Tags
— (Array<map>
)Creates tags for the packaging job.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
createEndpoint(params = {}, callback) ⇒ AWS.Request
Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API to deploy models using SageMaker hosting services.
For an example that calls this method when deploying a model to SageMaker hosting services, see the Create Endpoint example notebook.
Note: You must not delete anEndpointConfig
that is in use by an endpoint that is live or while theUpdateEndpoint
orCreateEndpoint
operations are being performed on the endpoint. To update an endpoint, you must create a newEndpointConfig
.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.
Note: 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 supportingEventually 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 toInService
. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
Note: To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.- Option 1: For a full SageMaker access, search and attach the
AmazonSageMakerFullAccess
policy. - Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:
"Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]
"Resource": [
"arn:aws:sagemaker:region:account-id:endpoint/endpointName"
"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
]
For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.
Service Reference:
Examples:
Calling the createEndpoint operation
var params = { EndpointConfigName: 'STRING_VALUE', /* required */ EndpointName: 'STRING_VALUE', /* required */ DeploymentConfig: { BlueGreenUpdatePolicy: { /* required */ TrafficRoutingConfiguration: { /* required */ Type: ALL_AT_ONCE | CANARY | LINEAR, /* required */ WaitIntervalInSeconds: 'NUMBER_VALUE', /* required */ CanarySize: { Type: INSTANCE_COUNT | CAPACITY_PERCENT, /* required */ Value: 'NUMBER_VALUE' /* required */ }, LinearStepSize: { Type: INSTANCE_COUNT | CAPACITY_PERCENT, /* required */ Value: 'NUMBER_VALUE' /* required */ } }, MaximumExecutionTimeoutInSeconds: 'NUMBER_VALUE', TerminationWaitInSeconds: 'NUMBER_VALUE' }, AutoRollbackConfiguration: { Alarms: [ { AlarmName: 'STRING_VALUE' }, /* more items */ ] } }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createEndpoint(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointName
— (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 .EndpointConfigName
— (String
)The name of an endpoint configuration. For more information, see CreateEndpointConfig.
DeploymentConfig
— (map
)The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.
BlueGreenUpdatePolicy
— required — (map
)Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.
TrafficRoutingConfiguration
— required — (map
)Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.
Type
— required — (String
)Traffic routing strategy type.
-
ALL_AT_ONCE
: Endpoint traffic shifts to the new fleet in a single step. -
CANARY
: Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic. -
LINEAR
: Endpoint traffic shifts to the new fleet in n steps of a configurable size.
"ALL_AT_ONCE"
"CANARY"
"LINEAR"
-
WaitIntervalInSeconds
— required — (Integer
)The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.
CanarySize
— (map
)Batch size for the first step to turn on traffic on the new endpoint fleet.
Value
must be less than or equal to 50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
LinearStepSize
— (map
)Batch size for each step to turn on traffic on the new endpoint fleet.
Value
must be 10-50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
TerminationWaitInSeconds
— (Integer
)Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.
MaximumExecutionTimeoutInSeconds
— (Integer
)Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in
TerminationWaitInSeconds
andWaitIntervalInSeconds
.
AutoRollbackConfiguration
— (map
)Automatic rollback configuration for handling endpoint deployment failures and recovery.
Alarms
— (Array<map>
)List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.
AlarmName
— (String
)The name of a CloudWatch alarm in your account.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EndpointArn
— (String
)The Amazon Resource Name (ARN) of the endpoint.
-
(AWS.Response)
—
Returns:
createEndpointConfig(params = {}, callback) ⇒ AWS.Request
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.Note: 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. EachProductionVariant
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.Note: 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 supportingEventually 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.Service Reference:
Examples:
Calling the createEndpointConfig operation
var params = { EndpointConfigName: 'STRING_VALUE', /* required */ ProductionVariants: [ /* required */ { ModelName: 'STRING_VALUE', /* required */ VariantName: 'STRING_VALUE', /* required */ AcceleratorType: ml.eia1.medium | ml.eia1.large | ml.eia1.xlarge | ml.eia2.medium | ml.eia2.large | ml.eia2.xlarge, CoreDumpConfig: { DestinationS3Uri: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, InitialInstanceCount: 'NUMBER_VALUE', InitialVariantWeight: 'NUMBER_VALUE', 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, ServerlessConfig: { MaxConcurrency: 'NUMBER_VALUE', /* required */ MemorySizeInMB: 'NUMBER_VALUE' /* required */ } }, /* more items */ ], AsyncInferenceConfig: { OutputConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', NotificationConfig: { ErrorTopic: 'STRING_VALUE', SuccessTopic: 'STRING_VALUE' } }, ClientConfig: { MaxConcurrentInvocationsPerInstance: 'NUMBER_VALUE' } }, DataCaptureConfig: { CaptureOptions: [ /* required */ { CaptureMode: Input | Output /* required */ }, /* more items */ ], DestinationS3Uri: 'STRING_VALUE', /* required */ InitialSamplingPercentage: 'NUMBER_VALUE', /* required */ CaptureContentTypeHeader: { CsvContentTypes: [ 'STRING_VALUE', /* more items */ ], JsonContentTypes: [ 'STRING_VALUE', /* more items */ ] }, EnableCapture: true || false, KmsKeyId: 'STRING_VALUE' }, KmsKeyId: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createEndpointConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointConfigName
— (String
)The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
ProductionVariants
— (Array<map>
)An list of
ProductionVariant
objects, one for each model that you want to host at this endpoint.VariantName
— required — (String
)The name of the production variant.
ModelName
— required — (String
)The name of the model that you want to host. This is the name that you specified when creating the model.
InitialInstanceCount
— (Integer
)Number of instances to launch initially.
InstanceType
— (String
)The ML compute instance type.
Possible values include:"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"
InitialVariantWeight
— (Float
)Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the
VariantWeight
to the sum of allVariantWeight
values across all ProductionVariants. If unspecified, it defaults to 1.0.AcceleratorType
— (String
)The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
Possible values include:"ml.eia1.medium"
"ml.eia1.large"
"ml.eia1.xlarge"
"ml.eia2.medium"
"ml.eia2.large"
"ml.eia2.xlarge"
CoreDumpConfig
— (map
)Specifies configuration for a core dump from the model container when the process crashes.
DestinationS3Uri
— required — (String
)The Amazon S3 bucket to send the core dump to.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys forOutputDataConfig
. If you use a bucket policy with ans3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint
andUpdateEndpoint
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
ServerlessConfig
— (map
)The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
DataCaptureConfig
— (map
)Configuration to control how SageMaker captures inference data.
EnableCapture
— (Boolean
)Whether data capture should be enabled or disabled (defaults to enabled).
InitialSamplingPercentage
— required — (Integer
)The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.
DestinationS3Uri
— required — (String
)The Amazon S3 location used to capture the data.
KmsKeyId
— (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
-
CaptureOptions
— required — (Array<map>
)Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both
CaptureMode
— required — (String
)Specify the boundary of data to capture.
Possible values include:"Input"
"Output"
CaptureContentTypeHeader
— (map
)Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.
CsvContentTypes
— (Array<String>
)The list of all content type headers that SageMaker will treat as CSV and capture accordingly.
JsonContentTypes
— (Array<String>
)The list of all content type headers that SageMaker will treat as JSON and capture accordingly.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
KmsKeyId
— (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
,UpdateEndpoint
requests. For more information, refer to the Amazon Web Services Key Management Service section Using Key Policies in Amazon Web Services KMSNote: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aKmsKeyId
when using an instance type with local storage. If any of the models that you specify in theProductionVariants
parameter use nitro-based instances with local storage, do not specify a value for theKmsKeyId
parameter. If you specify a value forKmsKeyId
when using any nitro-based instances with local storage, the call toCreateEndpointConfig
fails. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.-
AsyncInferenceConfig
— (map
)Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync.
ClientConfig
— (map
)Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
MaxConcurrentInvocationsPerInstance
— (Integer
)The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.
OutputConfig
— required — (map
)Specifies the configuration for asynchronous inference invocation outputs.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
S3OutputPath
— required — (String
)The Amazon S3 location to upload inference responses to.
NotificationConfig
— (map
)Specifies the configuration for notifications of inference results for asynchronous inference.
SuccessTopic
— (String
)Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
ErrorTopic
— (String
)Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EndpointConfigArn
— (String
)The Amazon Resource Name (ARN) of the endpoint configuration.
-
(AWS.Response)
—
Returns:
createExperiment(params = {}, callback) ⇒ AWS.Request
Creates an 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.
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.
Service Reference:
Examples:
Calling the createExperiment operation
var params = { ExperimentName: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', DisplayName: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createExperiment(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ExperimentName
— (String
)The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.
DisplayName
— (String
)The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify
DisplayName
, the value inExperimentName
is displayed.Description
— (String
)The description of the experiment.
Tags
— (Array<map>
)A list of tags to associate with the experiment. You can use Search API to search on the tags.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ExperimentArn
— (String
)The Amazon Resource Name (ARN) of the experiment.
-
(AWS.Response)
—
Returns:
createFeatureGroup(params = {}, callback) ⇒ AWS.Request
Create a new
FeatureGroup
. AFeatureGroup
is a group ofFeatures
defined in theFeatureStore
to describe aRecord
.The
FeatureGroup
defines the schema and features contained in the FeatureGroup. AFeatureGroup
definition is composed of a list ofFeatures
, aRecordIdentifierFeatureName
, anEventTimeFeatureName
and configurations for itsOnlineStore
andOfflineStore
. Check Amazon Web Services service quotas to see theFeatureGroup
s quota for your Amazon Web Services account.You must include at least one of
OnlineStoreConfig
andOfflineStoreConfig
to create aFeatureGroup
.Service Reference:
Examples:
Calling the createFeatureGroup operation
var params = { EventTimeFeatureName: 'STRING_VALUE', /* required */ FeatureDefinitions: [ /* required */ { FeatureName: 'STRING_VALUE', FeatureType: Integral | Fractional | String }, /* more items */ ], FeatureGroupName: 'STRING_VALUE', /* required */ RecordIdentifierFeatureName: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', OfflineStoreConfig: { S3StorageConfig: { /* required */ S3Uri: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', ResolvedOutputS3Uri: 'STRING_VALUE' }, DataCatalogConfig: { Catalog: 'STRING_VALUE', /* required */ Database: 'STRING_VALUE', /* required */ TableName: 'STRING_VALUE' /* required */ }, DisableGlueTableCreation: true || false }, OnlineStoreConfig: { EnableOnlineStore: true || false, SecurityConfig: { KmsKeyId: 'STRING_VALUE' } }, RoleArn: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createFeatureGroup(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
FeatureGroupName
— (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.
-
RecordIdentifierFeatureName
— (String
)The name of the
Feature
whose value uniquely identifies aRecord
defined in theFeatureStore
. Only the latest record per identifier value will be stored in theOnlineStore
.RecordIdentifierFeatureName
must be one of feature definitions' names.You use the
RecordIdentifierFeatureName
to 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.
-
EventTimeFeatureName
— (String
)The name of the feature that stores the
EventTime
of aRecord
in aFeatureGroup
.An
EventTime
is a point in time when a new event occurs that corresponds to the creation or update of aRecord
in aFeatureGroup
. AllRecords
in theFeatureGroup
must have a correspondingEventTime
.An
EventTime
can be aString
orFractional
.-
Fractional
:EventTime
feature values must be a Unix timestamp in seconds. -
String
:EventTime
feature values must be an ISO-8601 string in the format. The following formats are supportedyyyy-MM-dd'T'HH:mm:ssZ
andyyyy-MM-dd'T'HH:mm:ss.SSSZ
whereyyyy
,MM
, anddd
represent the year, month, and day respectively andHH
,mm
,ss
, and if applicable,SSS
represent the hour, month, second and milliseconds respsectively.'T'
andZ
are constants.
-
FeatureDefinitions
— (Array<map>
)A list of
Feature
names and types.Name
andType
is compulsory perFeature
.Valid feature
FeatureType
s areIntegral
,Fractional
andString
.FeatureName
s cannot be any of the following:is_deleted
,write_time
,api_invocation_time
You can create up to 2,500
FeatureDefinition
s perFeatureGroup
.FeatureName
— (String
)The name of a feature. The type must be a string.
FeatureName
cannot be any of the following:is_deleted
,write_time
,api_invocation_time
.FeatureType
— (String
)The value type of a feature. Valid values are Integral, Fractional, or String.
Possible values include:"Integral"
"Fractional"
"String"
OnlineStoreConfig
— (map
)You can turn the
OnlineStore
on or off by specifyingTrue
for theEnableOnlineStore
flag inOnlineStoreConfig
; the default value isFalse
.You can also include an Amazon Web Services KMS key ID (
KMSKeyId
) for at-rest encryption of theOnlineStore
.SecurityConfig
— (map
)Use to specify KMS Key ID (
KMSKeyId
) for at-rest encryption of yourOnlineStore
.KmsKeyId
— (String
)The ID of the Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either IAM user or IAM role) of
CreateFeatureGroup
must have below permissions to theOnlineStore
KmsKeyId
:-
"kms:Encrypt"
-
"kms:Decrypt"
-
"kms:DescribeKey"
-
"kms:CreateGrant"
-
"kms:RetireGrant"
-
"kms:ReEncryptFrom"
-
"kms:ReEncryptTo"
-
"kms:GenerateDataKey"
-
"kms:ListAliases"
-
"kms:ListGrants"
-
"kms:RevokeGrant"
The caller (either IAM user or IAM role) to all DataPlane operations (
PutRecord
,GetRecord
,DeleteRecord
) must have the following permissions to theKmsKeyId
:-
"kms:Decrypt"
-
EnableOnlineStore
— (Boolean
)Turn
OnlineStore
off by specifyingFalse
for theEnableOnlineStore
flag. TurnOnlineStore
on by specifyingTrue
for theEnableOnlineStore
flag.The default value is
False
.
OfflineStoreConfig
— (map
)Use this to configure an
OfflineFeatureStore
. This parameter allows you to specify:-
The Amazon Simple Storage Service (Amazon S3) location of an
OfflineStore
. -
A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog.
-
An KMS encryption key to encrypt the Amazon S3 location used for
OfflineStore
. If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent.
To learn more about this parameter, see OfflineStoreConfig.
S3StorageConfig
— required — (map
)The Amazon Simple Storage (Amazon S3) location of
OfflineStore
.S3Uri
— required — (String
)The S3 URI, or location in Amazon S3, of
OfflineStore
.S3 URIs have a format similar to the following:
s3://example-bucket/prefix/
.KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) key ID of the key used to encrypt any objects written into the
OfflineStore
S3 location.The IAM
roleARN
that is passed as a parameter toCreateFeatureGroup
must have below permissions to theKmsKeyId
:-
"kms:GenerateDataKey"
-
ResolvedOutputS3Uri
— (String
)The S3 path where offline records are written.
DisableGlueTableCreation
— (Boolean
)Set to
True
to disable the automatic creation of an Amazon Web Services Glue table when configuring anOfflineStore
.DataCatalogConfig
— (map
)The meta data of the Glue table that is autogenerated when an
OfflineStore
is created.TableName
— required — (String
)The name of the Glue table.
Catalog
— required — (String
)The name of the Glue table catalog.
Database
— required — (String
)The name of the Glue table database.
-
RoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the
OfflineStore
if anOfflineStoreConfig
is provided.Description
— (String
)A free-form description of a
FeatureGroup
.Tags
— (Array<map>
)Tags used to identify
Features
in eachFeatureGroup
.Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:FeatureGroupArn
— (String
)The Amazon Resource Name (ARN) of the
FeatureGroup
. This is a unique identifier for the feature group.
-
(AWS.Response)
—
Returns:
createFlowDefinition(params = {}, callback) ⇒ AWS.Request
Creates a flow definition.
Service Reference:
Examples:
Calling the createFlowDefinition operation
var params = { FlowDefinitionName: 'STRING_VALUE', /* required */ HumanLoopConfig: { /* required */ HumanTaskUiArn: 'STRING_VALUE', /* required */ TaskCount: 'NUMBER_VALUE', /* required */ TaskDescription: 'STRING_VALUE', /* required */ TaskTitle: 'STRING_VALUE', /* required */ WorkteamArn: 'STRING_VALUE', /* required */ PublicWorkforceTaskPrice: { AmountInUsd: { Cents: 'NUMBER_VALUE', Dollars: 'NUMBER_VALUE', TenthFractionsOfACent: 'NUMBER_VALUE' } }, TaskAvailabilityLifetimeInSeconds: 'NUMBER_VALUE', TaskKeywords: [ 'STRING_VALUE', /* more items */ ], TaskTimeLimitInSeconds: 'NUMBER_VALUE' }, OutputConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ HumanLoopActivationConfig: { HumanLoopActivationConditionsConfig: { /* required */ HumanLoopActivationConditions: any /* This value will be JSON encoded on your behalf with JSON.stringify() */ /* required */ } }, HumanLoopRequestSource: { AwsManagedHumanLoopRequestSource: AWS/Rekognition/DetectModerationLabels/Image/V3 | AWS/Textract/AnalyzeDocument/Forms/V1 /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createFlowDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
FlowDefinitionName
— (String
)The name of your flow definition.
HumanLoopRequestSource
— (map
)Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
AwsManagedHumanLoopRequestSource
— required — (String
)Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:
Possible values include:"AWS/Rekognition/DetectModerationLabels/Image/V3"
"AWS/Textract/AnalyzeDocument/Forms/V1"
HumanLoopActivationConfig
— (map
)An object containing information about the events that trigger a human workflow.
HumanLoopActivationConditionsConfig
— required — (map
)Container structure for defining under what conditions SageMaker creates a human loop.
HumanLoopActivationConditions
— required — (String
)JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide.
HumanLoopConfig
— (map
)An object containing information about the tasks the human reviewers will perform.
WorkteamArn
— required — (String
)Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces.
HumanTaskUiArn
— required — (String
)The Amazon Resource Name (ARN) of the human task user interface.
You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI.
To learn how to create a custom HTML template, see Create Custom Worker Task Template.
To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates.
TaskTitle
— required — (String
)A title for the human worker task.
TaskDescription
— required — (String
)A description for the human worker task.
TaskCount
— required — (Integer
)The number of distinct workers who will perform the same task on each object. For example, if
TaskCount
is set to3
for an image classification labeling job, three workers will classify each input image. IncreasingTaskCount
can improve label accuracy.TaskAvailabilityLifetimeInSeconds
— (Integer
)The length of time that a task remains available for review by human workers.
TaskTimeLimitInSeconds
— (Integer
)The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).
TaskKeywords
— (Array<String>
)Keywords used to describe the task so that workers can discover the task.
PublicWorkforceTaskPrice
— (map
)Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.
Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.
-
0.036
-
0.048
-
0.060
-
0.072
-
0.120
-
0.240
-
0.360
-
0.480
-
0.600
-
0.720
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.
-
0.012
-
0.024
-
0.036
-
0.048
-
0.060
-
0.072
-
0.120
-
0.240
-
0.360
-
0.480
-
0.600
-
0.720
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.
-
2.400
-
2.280
-
2.160
-
2.040
-
1.920
-
1.800
-
1.680
-
1.560
-
1.440
-
1.320
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
AmountInUsd
— (map
)Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
Dollars
— (Integer
)The whole number of dollars in the amount.
Cents
— (Integer
)The fractional portion, in cents, of the amount.
TenthFractionsOfACent
— (Integer
)Fractions of a cent, in tenths.
-
OutputConfig
— (map
)An object containing information about where the human review results will be uploaded.
S3OutputPath
— required — (String
)The Amazon S3 path where the object containing human output will be made available.
To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data.
KmsKeyId
— (String
)The Amazon Key Management Service (KMS) key ID for server-side encryption.
RoleArn
— (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
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:FlowDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the flow definition you create.
-
(AWS.Response)
—
Returns:
createHumanTaskUi(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the createHumanTaskUi operation
var params = { HumanTaskUiName: 'STRING_VALUE', /* required */ UiTemplate: { /* required */ Content: 'STRING_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createHumanTaskUi(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HumanTaskUiName
— (String
)The name of the user interface you are creating.
UiTemplate
— (map
)The Liquid template for the worker user interface.
Content
— required — (String
)The content of the Liquid template for the worker user interface.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HumanTaskUiArn
— (String
)The Amazon Resource Name (ARN) of the human review workflow user interface you create.
-
(AWS.Response)
—
Returns:
createHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the createHyperParameterTuningJob operation
var params = { HyperParameterTuningJobConfig: { /* required */ ResourceLimits: { /* required */ MaxNumberOfTrainingJobs: 'NUMBER_VALUE', /* required */ MaxParallelTrainingJobs: 'NUMBER_VALUE' /* required */ }, Strategy: Bayesian | Random, /* required */ HyperParameterTuningJobObjective: { MetricName: 'STRING_VALUE', /* required */ Type: Maximize | Minimize /* required */ }, ParameterRanges: { CategoricalParameterRanges: [ { Name: 'STRING_VALUE', /* required */ Values: [ /* required */ 'STRING_VALUE', /* more items */ ] }, /* more items */ ], ContinuousParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ], IntegerParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ] }, TrainingJobEarlyStoppingType: Off | Auto, TuningJobCompletionCriteria: { TargetObjectiveMetricValue: 'NUMBER_VALUE' /* required */ } }, HyperParameterTuningJobName: 'STRING_VALUE', /* required */ Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], TrainingJobDefinition: { AlgorithmSpecification: { /* required */ TrainingInputMode: Pipe | File | FastFile, /* required */ AlgorithmName: 'STRING_VALUE', MetricDefinitions: [ { Name: 'STRING_VALUE', /* required */ Regex: 'STRING_VALUE' /* required */ }, /* more items */ ], TrainingImage: 'STRING_VALUE' }, OutputDataConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, ResourceConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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, /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ StoppingCondition: { /* required */ MaxRuntimeInSeconds: 'NUMBER_VALUE', MaxWaitTimeInSeconds: 'NUMBER_VALUE' }, CheckpointConfig: { S3Uri: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE' }, DefinitionName: 'STRING_VALUE', EnableInterContainerTrafficEncryption: true || false, EnableManagedSpotTraining: true || false, EnableNetworkIsolation: true || false, HyperParameterRanges: { CategoricalParameterRanges: [ { Name: 'STRING_VALUE', /* required */ Values: [ /* required */ 'STRING_VALUE', /* more items */ ] }, /* more items */ ], ContinuousParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ], IntegerParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ] }, InputDataConfig: [ { ChannelName: 'STRING_VALUE', /* required */ DataSource: { /* required */ FileSystemDataSource: { DirectoryPath: 'STRING_VALUE', /* required */ FileSystemAccessMode: rw | ro, /* required */ FileSystemId: 'STRING_VALUE', /* required */ FileSystemType: EFS | FSxLustre /* required */ }, S3DataSource: { S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE', /* required */ AttributeNames: [ 'STRING_VALUE', /* more items */ ], S3DataDistributionType: FullyReplicated | ShardedByS3Key } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', InputMode: Pipe | File | FastFile, RecordWrapperType: None | RecordIO, ShuffleConfig: { Seed: 'NUMBER_VALUE' /* required */ } }, /* more items */ ], RetryStrategy: { MaximumRetryAttempts: 'NUMBER_VALUE' /* required */ }, StaticHyperParameters: { '<HyperParameterKey>': 'STRING_VALUE', /* '<HyperParameterKey>': ... */ }, TuningObjective: { MetricName: 'STRING_VALUE', /* required */ Type: Maximize | Minimize /* required */ }, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, TrainingJobDefinitions: [ { AlgorithmSpecification: { /* required */ TrainingInputMode: Pipe | File | FastFile, /* required */ AlgorithmName: 'STRING_VALUE', MetricDefinitions: [ { Name: 'STRING_VALUE', /* required */ Regex: 'STRING_VALUE' /* required */ }, /* more items */ ], TrainingImage: 'STRING_VALUE' }, OutputDataConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, ResourceConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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, /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ StoppingCondition: { /* required */ MaxRuntimeInSeconds: 'NUMBER_VALUE', MaxWaitTimeInSeconds: 'NUMBER_VALUE' }, CheckpointConfig: { S3Uri: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE' }, DefinitionName: 'STRING_VALUE', EnableInterContainerTrafficEncryption: true || false, EnableManagedSpotTraining: true || false, EnableNetworkIsolation: true || false, HyperParameterRanges: { CategoricalParameterRanges: [ { Name: 'STRING_VALUE', /* required */ Values: [ /* required */ 'STRING_VALUE', /* more items */ ] }, /* more items */ ], ContinuousParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ], IntegerParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ] }, InputDataConfig: [ { ChannelName: 'STRING_VALUE', /* required */ DataSource: { /* required */ FileSystemDataSource: { DirectoryPath: 'STRING_VALUE', /* required */ FileSystemAccessMode: rw | ro, /* required */ FileSystemId: 'STRING_VALUE', /* required */ FileSystemType: EFS | FSxLustre /* required */ }, S3DataSource: { S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE', /* required */ AttributeNames: [ 'STRING_VALUE', /* more items */ ], S3DataDistributionType: FullyReplicated | ShardedByS3Key } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', InputMode: Pipe | File | FastFile, RecordWrapperType: None | RecordIO, ShuffleConfig: { Seed: 'NUMBER_VALUE' /* required */ } }, /* more items */ ], RetryStrategy: { MaximumRetryAttempts: 'NUMBER_VALUE' /* required */ }, StaticHyperParameters: { '<HyperParameterKey>': 'STRING_VALUE', /* '<HyperParameterKey>': ... */ }, TuningObjective: { MetricName: 'STRING_VALUE', /* required */ Type: Maximize | Minimize /* required */ }, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, /* more items */ ], WarmStartConfig: { ParentHyperParameterTuningJobs: [ /* required */ { HyperParameterTuningJobName: 'STRING_VALUE' }, /* more items */ ], WarmStartType: IdenticalDataAndAlgorithm | TransferLearning /* required */ } }; sagemaker.createHyperParameterTuningJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HyperParameterTuningJobName
— (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.
HyperParameterTuningJobConfig
— (map
)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.
Strategy
— required — (String
)Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. To use the Bayesian search strategy, set this to
Possible values include:Bayesian
. To randomly search, set it toRandom
. For information about search strategies, see How Hyperparameter Tuning Works."Bayesian"
"Random"
HyperParameterTuningJobObjective
— (map
)The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
ResourceLimits
— required — (map
)The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.
MaxNumberOfTrainingJobs
— required — (Integer
)The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs
— required — (Integer
)The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
ParameterRanges
— (map
)The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
TrainingJobEarlyStoppingType
— (String
)Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be one of the following values (the default value is
OFF
):- OFF
-
Training jobs launched by the hyperparameter tuning job do not use early stopping.
- AUTO
-
SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.
"Off"
"Auto"
TuningJobCompletionCriteria
— (map
)The tuning job's completion criteria.
TargetObjectiveMetricValue
— required — (Float
)The value of the objective metric.
TrainingJobDefinition
— (map
)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.
DefinitionName
— (String
)The job definition name.
TuningObjective
— (map
)Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the
Type
parameter.Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
HyperParameterRanges
— (map
)Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note: You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job can search over. Every possible value of a categorical parameter range counts against this limit.IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
StaticHyperParameters
— (map<String>
)Specifies the values of hyperparameters that do not change for the tuning job.
AlgorithmSpecification
— required — (map
)The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
AlgorithmName
— (String
)The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for
TrainingImage
.MetricDefinitions
— (Array<map>
)An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig
— (Array<map>
)An array of Channel objects that specify the input for the training jobs that the tuning job launches.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
VpcConfig
— (map
)The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
OutputDataConfig
— required — (map
)Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys forOutputDataConfig
. If you use a bucket policy with ans3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.The KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.
ResourceConfig
— required — (map
)The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.InstanceType
— required — (String
)The ML compute instance type.
Possible values include:"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"
InstanceCount
— required — (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.You must specify sufficient ML storage for your scenario.
Note: SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.Note: Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request aVolumeSizeInGB
greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
StoppingCondition
— required — (map
)Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.
EnableNetworkIsolation
— (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 network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption
— (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.EnableManagedSpotTraining
— (Boolean
)A Boolean indicating whether managed spot training is enabled (
True
) or not (False
).CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
TrainingJobDefinitions
— (Array<map>
)A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
DefinitionName
— (String
)The job definition name.
TuningObjective
— (map
)Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the
Type
parameter.Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
HyperParameterRanges
— (map
)Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note: You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job can search over. Every possible value of a categorical parameter range counts against this limit.IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
StaticHyperParameters
— (map<String>
)Specifies the values of hyperparameters that do not change for the tuning job.
AlgorithmSpecification
— required — (map
)The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
AlgorithmName
— (String
)The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for
TrainingImage
.MetricDefinitions
— (Array<map>
)An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig
— (Array<map>
)An array of Channel objects that specify the input for the training jobs that the tuning job launches.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
VpcConfig
— (map
)The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
OutputDataConfig
— required — (map
)Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys forOutputDataConfig
. If you use a bucket policy with ans3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.The KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.
ResourceConfig
— required — (map
)The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.InstanceType
— required — (String
)The ML compute instance type.
Possible values include:"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"
InstanceCount
— required — (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.You must specify sufficient ML storage for your scenario.
Note: SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.Note: Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request aVolumeSizeInGB
greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
StoppingCondition
— required — (map
)Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.
EnableNetworkIsolation
— (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 network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption
— (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.EnableManagedSpotTraining
— (Boolean
)A Boolean indicating whether managed spot training is enabled (
True
) or not (False
).CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
WarmStartConfig
— (map
)Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify
IDENTICAL_DATA_AND_ALGORITHM
as theWarmStartType
value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.Note: 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.ParentHyperParameterTuningJobs
— required — (Array<map>
)An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
HyperParameterTuningJobName
— (String
)The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
WarmStartType
— required — (String
)Specifies one of the following:
- IDENTICAL_DATA_AND_ALGORITHM
-
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
- TRANSFER_LEARNING
-
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
"IdenticalDataAndAlgorithm"
"TransferLearning"
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HyperParameterTuningJobArn
— (String
)The Amazon Resource Name (ARN) of the tuning job. SageMaker assigns an ARN to a hyperparameter tuning job when you create it.
-
(AWS.Response)
—
Returns:
createImage(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the createImage operation
var params = { ImageName: 'STRING_VALUE', /* required */ RoleArn: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', DisplayName: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createImage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
Description
— (String
)The description of the image.
DisplayName
— (String
)The display name of the image. If not provided,
ImageName
is displayed.ImageName
— (String
)The name of the image. Must be unique to your account.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
Tags
— (Array<map>
)A list of tags to apply to the image.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ImageArn
— (String
)The Amazon Resource Name (ARN) of the image.
-
(AWS.Response)
—
Returns:
createImageVersion(params = {}, callback) ⇒ AWS.Request
Creates a version of the SageMaker image specified by
ImageName
. The version represents the Amazon Elastic Container Registry (ECR) container image specified byBaseImage
.Service Reference:
Examples:
Calling the createImageVersion operation
var params = { BaseImage: 'STRING_VALUE', /* required */ ClientToken: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE' /* required */ }; sagemaker.createImageVersion(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
BaseImage
— (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
— (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.
If a token is not provided, the SDK will use a version 4 UUID.ImageName
— (String
)The
ImageName
of theImage
to create a version of.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ImageVersionArn
— (String
)The Amazon Resource Name (ARN) of the image version.
-
(AWS.Response)
—
Returns:
createInferenceRecommendationsJob(params = {}, callback) ⇒ AWS.Request
Starts a recommendation job. You can create either an instance recommendation or load test job.
Service Reference:
Examples:
Calling the createInferenceRecommendationsJob operation
var params = { InputConfig: { /* required */ ModelPackageVersionArn: 'STRING_VALUE', /* required */ EndpointConfigurations: [ { 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, /* required */ EnvironmentParameterRanges: { CategoricalParameterRanges: [ { Name: 'STRING_VALUE', /* required */ Value: [ /* required */ 'STRING_VALUE', /* more items */ ] }, /* more items */ ] }, InferenceSpecificationName: 'STRING_VALUE' }, /* more items */ ], JobDurationInSeconds: 'NUMBER_VALUE', ResourceLimit: { MaxNumberOfTests: 'NUMBER_VALUE', MaxParallelOfTests: 'NUMBER_VALUE' }, TrafficPattern: { Phases: [ { DurationInSeconds: 'NUMBER_VALUE', InitialNumberOfUsers: 'NUMBER_VALUE', SpawnRate: 'NUMBER_VALUE' }, /* more items */ ], TrafficType: PHASES }, VolumeKmsKeyId: 'STRING_VALUE' }, JobName: 'STRING_VALUE', /* required */ JobType: Default | Advanced, /* required */ RoleArn: 'STRING_VALUE', /* required */ JobDescription: 'STRING_VALUE', OutputConfig: { CompiledOutputConfig: { S3OutputUri: 'STRING_VALUE' }, KmsKeyId: 'STRING_VALUE' }, StoppingConditions: { MaxInvocations: 'NUMBER_VALUE', ModelLatencyThresholds: [ { Percentile: 'STRING_VALUE', ValueInMilliseconds: 'NUMBER_VALUE' }, /* more items */ ] }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createInferenceRecommendationsJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobName
— (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.
JobType
— (String
)Defines the type of recommendation job. Specify
Possible values include:Default
to initiate an instance recommendation andAdvanced
to initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will run an instance recommendation (DEFAULT
) job."Default"
"Advanced"
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
InputConfig
— (map
)Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.
ModelPackageVersionArn
— required — (String
)The Amazon Resource Name (ARN) of a versioned model package.
JobDurationInSeconds
— (Integer
)Specifies the maximum duration of the job, in seconds.>
TrafficPattern
— (map
)Specifies the traffic pattern of the job.
TrafficType
— (String
)Defines the traffic patterns.
Possible values include:"PHASES"
Phases
— (Array<map>
)Defines the phases traffic specification.
InitialNumberOfUsers
— (Integer
)Specifies how many concurrent users to start with.
SpawnRate
— (Integer
)Specified how many new users to spawn in a minute.
DurationInSeconds
— (Integer
)Specifies how long traffic phase should be.
ResourceLimit
— (map
)Defines the resource limit of the job.
MaxNumberOfTests
— (Integer
)Defines the maximum number of load tests.
MaxParallelOfTests
— (Integer
)Defines the maximum number of parallel load tests.
EndpointConfigurations
— (Array<map>
)Specifies the endpoint configuration to use for a job.
InstanceType
— required — (String
)The instance types to use for the load test.
Possible values include:"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"
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
EnvironmentParameterRanges
— (map
)The parameter you want to benchmark against.
CategoricalParameterRanges
— (Array<map>
)Specified a list of parameters for each category.
Name
— required — (String
)The Name of the environment variable.
Value
— required — (Array<String>
)The list of values you can pass.
VolumeKmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. This key will be passed to SageMaker Hosting for endpoint creation.
The SageMaker execution role must have
kms:CreateGrant
permission in order to encrypt data on the storage volume of the endpoints created for inference recommendation. The inference recommendation job will fail asynchronously during endpoint configuration creation if the role passed does not havekms:CreateGrant
permission.The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"
For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
-
JobDescription
— (String
)Description of the recommendation job.
StoppingConditions
— (map
)A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.
MaxInvocations
— (Integer
)The maximum number of requests per minute expected for the endpoint.
ModelLatencyThresholds
— (Array<map>
)The interval of time taken by a model to respond as viewed from SageMaker. The interval includes the local communication time taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.
Percentile
— (String
)The model latency percentile threshold.
ValueInMilliseconds
— (Integer
)The model latency percentile value in milliseconds.
OutputConfig
— (map
)Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt your output artifacts with Amazon S3 server-side encryption. The SageMaker execution role must have
kms:GenerateDataKey
permission.The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"
For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
-
CompiledOutputConfig
— (map
)Provides information about the output configuration for the compiled model.
S3OutputUri
— (String
)Identifies the Amazon S3 bucket where you want SageMaker to store the compiled model artifacts.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobArn
— (String
)The Amazon Resource Name (ARN) of the recommendation job.
-
(AWS.Response)
—
Returns:
createLabelingJob(params = {}, callback) ⇒ AWS.Request
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.Service Reference:
Examples:
Calling the createLabelingJob operation
var params = { HumanTaskConfig: { /* required */ AnnotationConsolidationConfig: { /* required */ AnnotationConsolidationLambdaArn: 'STRING_VALUE' /* required */ }, NumberOfHumanWorkersPerDataObject: 'NUMBER_VALUE', /* required */ PreHumanTaskLambdaArn: 'STRING_VALUE', /* required */ TaskDescription: 'STRING_VALUE', /* required */ TaskTimeLimitInSeconds: 'NUMBER_VALUE', /* required */ TaskTitle: 'STRING_VALUE', /* required */ UiConfig: { /* required */ HumanTaskUiArn: 'STRING_VALUE', UiTemplateS3Uri: 'STRING_VALUE' }, WorkteamArn: 'STRING_VALUE', /* required */ MaxConcurrentTaskCount: 'NUMBER_VALUE', PublicWorkforceTaskPrice: { AmountInUsd: { Cents: 'NUMBER_VALUE', Dollars: 'NUMBER_VALUE', TenthFractionsOfACent: 'NUMBER_VALUE' } }, TaskAvailabilityLifetimeInSeconds: 'NUMBER_VALUE', TaskKeywords: [ 'STRING_VALUE', /* more items */ ] }, InputConfig: { /* required */ DataSource: { /* required */ S3DataSource: { ManifestS3Uri: 'STRING_VALUE' /* required */ }, SnsDataSource: { SnsTopicArn: 'STRING_VALUE' /* required */ } }, DataAttributes: { ContentClassifiers: [ FreeOfPersonallyIdentifiableInformation | FreeOfAdultContent, /* more items */ ] } }, LabelAttributeName: 'STRING_VALUE', /* required */ LabelingJobName: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', SnsTopicArn: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ LabelCategoryConfigS3Uri: 'STRING_VALUE', LabelingJobAlgorithmsConfig: { LabelingJobAlgorithmSpecificationArn: 'STRING_VALUE', /* required */ InitialActiveLearningModelArn: 'STRING_VALUE', LabelingJobResourceConfig: { VolumeKmsKeyId: 'STRING_VALUE', VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } } }, StoppingConditions: { MaxHumanLabeledObjectCount: 'NUMBER_VALUE', MaxPercentageOfInputDatasetLabeled: 'NUMBER_VALUE' }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createLabelingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
LabelingJobName
— (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.
LabelingJobName
is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.LabelAttributeName
— (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
LabelAttributeName
must meet the following requirements.-
The name can't end with "-metadata".
-
If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".
-
Image semantic segmentation (
SemanticSegmentation)
, and adjustment (AdjustmentSemanticSegmentation
) and verification (VerificationSemanticSegmentation
) labeling jobs for this task type. -
Video frame object detection (
VideoObjectDetection
), and adjustment and verification (AdjustmentVideoObjectDetection
) labeling jobs for this task type. -
Video frame object tracking (
VideoObjectTracking
), and adjustment and verification (AdjustmentVideoObjectTracking
) labeling jobs for this task type. -
3D point cloud semantic segmentation (
3DPointCloudSemanticSegmentation
), and adjustment and verification (Adjustment3DPointCloudSemanticSegmentation
) labeling jobs for this task type. -
3D point cloud object tracking (
3DPointCloudObjectTracking
), and adjustment and verification (Adjustment3DPointCloudObjectTracking
) labeling jobs for this task type.
-
If you are creating an adjustment or verification labeling job, you must use a different
LabelAttributeName
than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see Verify and Adjust Labels.-
InputConfig
— (map
)Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
You must specify at least one of the following:
S3DataSource
orSnsDataSource
.-
Use
SnsDataSource
to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in the input manifest file have been labeled. -
Use
S3DataSource
to specify an input manifest file for both streaming and one-time labeling jobs. Adding anS3DataSource
is optional if you useSnsDataSource
to create a streaming labeling job.
If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use
ContentClassifiers
to specify that your data is free of personally identifiable information and adult content.DataSource
— required — (map
)The location of the input data.
S3DataSource
— (map
)The Amazon S3 location of the input data objects.
ManifestS3Uri
— required — (String
)The Amazon S3 location of the manifest file that describes the input data objects.
The input manifest file referenced in
ManifestS3Uri
must contain one of the following keys:source-ref
orsource
. The value of the keys are interpreted as follows:-
source-ref
: The source of the object is the Amazon S3 object specified in the value. Use this value when the object is a binary object, such as an image. -
source
: The source of the object is the value. Use this value when the object is a text value.
If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.
-
SnsDataSource
— (map
)An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job.
SnsTopicArn
— required — (String
)The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.
DataAttributes
— (map
)Attributes of the data specified by the customer.
ContentClassifiers
— (Array<String>
)Declares that your content is free of personally identifiable information or adult content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
-
OutputConfig
— (map
)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.
S3OutputPath
— required — (String
)The Amazon S3 location to write output data.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service ID of the key used to encrypt the output data, if any.
If you provide your own KMS key ID, you must add the required permissions to your KMS key described in Encrypt Output Data and Storage Volume with Amazon Web Services KMS.
If you don't provide a KMS key ID, Amazon SageMaker uses the default Amazon Web Services KMS key for Amazon S3 for your role's account to encrypt your output data.
If you use a bucket policy with an
s3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.SnsTopicArn
— (String
)An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a
SnsTopicArn
if you want to do real time chaining to another streaming job and receive an Amazon SNS notifications each time a data object is submitted by a worker.If you provide an
SnsTopicArn
inOutputConfig
, when workers complete labeling tasks, Ground Truth will send labeling task output data to the SNS output topic you specify here.To learn more, see Receive Output Data from a Streaming Labeling Job.
RoleArn
— (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.
LabelCategoryConfigS3Uri
— (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_n
with your label categories.{
"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}
Note the following about the label category configuration file:
-
For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
-
Each label category must be unique, you cannot specify duplicate label categories.
-
If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include
auditLabelAttributeName
in the label category configuration. Use this parameter to enter theLabelAttributeName
of the labeling job you want to adjust or verify annotations of.
-
StoppingConditions
— (map
)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.
MaxHumanLabeledObjectCount
— (Integer
)The maximum number of objects that can be labeled by human workers.
MaxPercentageOfInputDatasetLabeled
— (Integer
)The maximum number of input data objects that should be labeled.
LabelingJobAlgorithmsConfig
— (map
)Configures the information required to perform automated data labeling.
LabelingJobAlgorithmSpecificationArn
— required — (String
)Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:
-
Image classification
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification
-
Text classification
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification
-
Object detection
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection
-
Semantic Segmentation
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation
-
InitialActiveLearningModelArn
— (String
)At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.
LabelingJobResourceConfig
— (map
)Provides configuration information for a labeling job.
VolumeKmsKeyId
— (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(s) that run the training and inference jobs used for automated data labeling.
You can only specify a
VolumeKmsKeyId
when you create a labeling job with automated data labeling enabled using the API operationCreateLabelingJob
. You cannot specify an Amazon Web Services KMS key to encrypt the storage volume used for automated data labeling model training and inference when you create a labeling job using the console. To learn more, see Output Data and Storage Volume Encryption.The
VolumeKmsKeyId
can be any of the following formats:-
KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
VpcConfig
— (map
)Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
HumanTaskConfig
— (map
)Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
WorkteamArn
— required — (String
)The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
UiConfig
— required — (map
)Information about the user interface that workers use to complete the labeling task.
UiTemplateS3Uri
— (String
)The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template.
HumanTaskUiArn
— (String
)The ARN of the worker task template used to render the worker UI and tools for labeling job tasks.
Use this parameter when you are creating a labeling job for named entity recognition, 3D point cloud and video frame labeling jobs. Use your labeling job task type to select one of the following ARNs and use it with this parameter when you create a labeling job. Replace
aws-region
with the Amazon Web Services Region you are creating your labeling job in. For example, replaceaws-region
withus-west-1
if you create a labeling job in US West (N. California).Named Entity Recognition
Use the following
HumanTaskUiArn
for named entity recognition labeling jobs:arn:aws:sagemaker:aws-region:394669845002:human-task-ui/NamedEntityRecognition
3D Point Cloud HumanTaskUiArns
Use this
HumanTaskUiArn
for 3D point cloud object detection and 3D point cloud object detection adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetection
Use this
HumanTaskUiArn
for 3D point cloud object tracking and 3D point cloud object tracking adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTracking
Use this
HumanTaskUiArn
for 3D point cloud semantic segmentation and 3D point cloud semantic segmentation adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentation
Video Frame HumanTaskUiArns
Use this
HumanTaskUiArn
for video frame object detection and video frame object detection adjustment labeling jobs.-
arn:aws:sagemaker:region:394669845002:human-task-ui/VideoObjectDetection
Use this
HumanTaskUiArn
for video frame object tracking and video frame object tracking adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/VideoObjectTracking
-
PreHumanTaskLambdaArn
— required — (String
)The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.
For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for
PreHumanTaskLambdaArn
. For custom labeling workflows, see Pre-annotation Lambda.Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClass
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetection
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking
3D Point Cloud Modalities
Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks. See 3D Point Cloud Task types to learn more.
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection
3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking
3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .
Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationBoundingBox
Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox
Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation
Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetection
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking
3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud frame.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection
3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking
3D point cloud semantic segmentation adjustment - Adjust semantic segmentation masks in a 3D point cloud.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
TaskKeywords
— (Array<String>
)Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.
TaskTitle
— required — (String
)A title for the task for your human workers.
TaskDescription
— required — (String
)A description of the task for your human workers.
NumberOfHumanWorkersPerDataObject
— required — (Integer
)The number of human workers that will label an object.
TaskTimeLimitInSeconds
— required — (Integer
)The amount of time that a worker has to complete a task.
If you create a custom labeling job, the maximum value for this parameter is 8 hours (28,800 seconds).
If you create a labeling job using a built-in task type the maximum for this parameter depends on the task type you use:
-
For image and text labeling jobs, the maximum is 8 hours (28,800 seconds).
-
For 3D point cloud and video frame labeling jobs, the maximum is 30 days (2952,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.
-
TaskAvailabilityLifetimeInSeconds
— (Integer
)The length of time that a task remains available for labeling by human workers. The default and maximum values for this parameter depend on the type of workforce you use.
-
If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours (43,200 seconds). The default is 6 hours (21,600 seconds).
-
If you choose a private or vendor workforce, the default value is 30 days (2592,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.
-
MaxConcurrentTaskCount
— (Integer
)Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects.
AnnotationConsolidationConfig
— required — (map
)Configures how labels are consolidated across human workers.
AnnotationConsolidationLambdaArn
— required — (String
)The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data.
This parameter is required for all labeling jobs. For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for
AnnotationConsolidationLambdaArn
. For custom labeling workflows, see Post-annotation Lambda.Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabel
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabel
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoMultiClass
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectDetection
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectTracking
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection
3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking
3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentation
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .
Semantic Segmentation Adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation
Semantic Segmentation Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation
Bounding Box Adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox
Bounding Box Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectDetection
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectTracking
3D Point Cloud Object Detection Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectDetection
3D Point Cloud Object Tracking Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects that appear in a sequence of 3D point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectTracking
3D Point Cloud Semantic Segmentation Adjustment - Use this task type when you want workers to adjust a point-level semantic segmentation masks using a paint tool.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
PublicWorkforceTaskPrice
— (map
)The price that you pay for each task performed by an Amazon Mechanical Turk worker.
AmountInUsd
— (map
)Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
Dollars
— (Integer
)The whole number of dollars in the amount.
Cents
— (Integer
)The fractional portion, in cents, of the amount.
TenthFractionsOfACent
— (Integer
)Fractions of a cent, in tenths.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:LabelingJobArn
— (String
)The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.
-
(AWS.Response)
—
Returns:
createModel(params = {}, callback) ⇒ AWS.Request
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 theCreateEndpoint
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.
Service Reference:
Examples:
Calling the createModel operation
var params = { ExecutionRoleArn: 'STRING_VALUE', /* required */ ModelName: 'STRING_VALUE', /* required */ Containers: [ { ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Image: 'STRING_VALUE', ImageConfig: { RepositoryAccessMode: Platform | Vpc, /* required */ RepositoryAuthConfig: { RepositoryCredentialsProviderArn: 'STRING_VALUE' /* required */ } }, InferenceSpecificationName: 'STRING_VALUE', Mode: SingleModel | MultiModel, ModelDataUrl: 'STRING_VALUE', ModelPackageName: 'STRING_VALUE', MultiModelConfig: { ModelCacheSetting: Enabled | Disabled } }, /* more items */ ], EnableNetworkIsolation: true || false, InferenceExecutionConfig: { Mode: Serial | Direct /* required */ }, PrimaryContainer: { ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Image: 'STRING_VALUE', ImageConfig: { RepositoryAccessMode: Platform | Vpc, /* required */ RepositoryAuthConfig: { RepositoryCredentialsProviderArn: 'STRING_VALUE' /* required */ } }, InferenceSpecificationName: 'STRING_VALUE', Mode: SingleModel | MultiModel, ModelDataUrl: 'STRING_VALUE', ModelPackageName: 'STRING_VALUE', MultiModelConfig: { ModelCacheSetting: Enabled | Disabled } }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }; sagemaker.createModel(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelName
— (String
)The name of the new model.
PrimaryContainer
— (map
)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.
ContainerHostname
— (String
)This parameter is ignored for models that contain only a
PrimaryContainer
.When a
ContainerDefinition
is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for aContainerDefinition
that is part of an inference pipeline, a unique name is automatically assigned based on the position of theContainerDefinition
in the pipeline. If you specify a value for theContainerHostName
for anyContainerDefinition
that is part of an inference pipeline, you must specify a value for theContainerHostName
parameter of everyContainerDefinition
in that pipeline.Image
— (String
)The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMakerImageConfig
— (map
)Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers
RepositoryAccessMode
— required — (String
)Set this to one of the following values:
-
Platform
- The model image is hosted in Amazon ECR. -
Vpc
- The model image is hosted in a private Docker registry in your VPC.
"Platform"
"Vpc"
-
RepositoryAuthConfig
— (map
)(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified
Vpc
as the value for theRepositoryAccessMode
field, and the private Docker registry where the model image is hosted requires authentication.RepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide.
Mode
— (String
)Whether the container hosts a single model or multiple models.
Possible values include:"SingleModel"
"MultiModel"
ModelDataUrl
— (String
)The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note: The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in
ModelDataUrl
.Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelPackageName
— (String
)The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
MultiModelConfig
— (map
)Specifies additional configuration for multi-model endpoints.
ModelCacheSetting
— (String
)Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to
Possible values include:Disabled
."Enabled"
"Disabled"
Containers
— (Array<map>
)Specifies the containers in the inference pipeline.
ContainerHostname
— (String
)This parameter is ignored for models that contain only a
PrimaryContainer
.When a
ContainerDefinition
is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for aContainerDefinition
that is part of an inference pipeline, a unique name is automatically assigned based on the position of theContainerDefinition
in the pipeline. If you specify a value for theContainerHostName
for anyContainerDefinition
that is part of an inference pipeline, you must specify a value for theContainerHostName
parameter of everyContainerDefinition
in that pipeline.Image
— (String
)The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMakerImageConfig
— (map
)Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers
RepositoryAccessMode
— required — (String
)Set this to one of the following values:
-
Platform
- The model image is hosted in Amazon ECR. -
Vpc
- The model image is hosted in a private Docker registry in your VPC.
"Platform"
"Vpc"
-
RepositoryAuthConfig
— (map
)(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified
Vpc
as the value for theRepositoryAccessMode
field, and the private Docker registry where the model image is hosted requires authentication.RepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide.
Mode
— (String
)Whether the container hosts a single model or multiple models.
Possible values include:"SingleModel"
"MultiModel"
ModelDataUrl
— (String
)The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note: The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in
ModelDataUrl
.Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelPackageName
— (String
)The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
MultiModelConfig
— (map
)Specifies additional configuration for multi-model endpoints.
ModelCacheSetting
— (String
)Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to
Possible values include:Disabled
."Enabled"
"Disabled"
InferenceExecutionConfig
— (map
)Specifies details of how containers in a multi-container endpoint are called.
Mode
— required — (String
)How containers in a multi-container are run. The following values are valid.
-
SERIAL
- Containers run as a serial pipeline. -
DIRECT
- Only the individual container that you specify is run.
"Serial"
"Direct"
-
ExecutionRoleArn
— (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.
Note: To be able to pass this role to SageMaker, the caller of this API must have theiam:PassRole
permission.Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
VpcConfig
— (map
)A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC.
VpcConfig
is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
EnableNetworkIsolation
— (Boolean
)Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelArn
— (String
)The ARN of the model created in SageMaker.
-
(AWS.Response)
—
Returns:
createModelBiasJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates the definition for a model bias job.
Service Reference:
Examples:
Calling the createModelBiasJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE', /* required */ JobResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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 */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, ModelBiasAppSpecification: { /* required */ ConfigUri: 'STRING_VALUE', /* required */ ImageUri: 'STRING_VALUE', /* required */ Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ } }, ModelBiasJobInput: { /* required */ EndpointInput: { /* required */ EndpointName: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' }, GroundTruthS3Input: { /* required */ S3Uri: 'STRING_VALUE' } }, ModelBiasJobOutputConfig: { /* required */ MonitoringOutputs: [ /* required */ { S3Output: { /* required */ LocalPath: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ ModelBiasBaselineConfig: { BaseliningJobName: 'STRING_VALUE', ConstraintsResource: { S3Uri: 'STRING_VALUE' } }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createModelBiasJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (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.
ModelBiasBaselineConfig
— (map
)The baseline configuration for a model bias job.
BaseliningJobName
— (String
)The name of the baseline model bias job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
ModelBiasAppSpecification
— (map
)Configures the model bias job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the model bias job.
ConfigUri
— required — (String
)JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
ModelBiasJobInput
— (map
)Inputs for the model bias job.
EndpointInput
— required — (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
GroundTruthS3Input
— required — (map
)Location of ground truth labels to use in model bias job.
S3Uri
— (String
)The address of the Amazon S3 location of the ground truth labels.
ModelBiasJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"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"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (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(s) that run the model monitoring job.
NetworkConfig
— (map
)Networking options for a model bias job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Tags
— (Array<map>
)(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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the model bias job.
-
(AWS.Response)
—
Returns:
createModelExplainabilityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates the definition for a model explainability job.
Service Reference:
Examples:
Calling the createModelExplainabilityJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE', /* required */ JobResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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 */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, ModelExplainabilityAppSpecification: { /* required */ ConfigUri: 'STRING_VALUE', /* required */ ImageUri: 'STRING_VALUE', /* required */ Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ } }, ModelExplainabilityJobInput: { /* required */ EndpointInput: { /* required */ EndpointName: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' } }, ModelExplainabilityJobOutputConfig: { /* required */ MonitoringOutputs: [ /* required */ { S3Output: { /* required */ LocalPath: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ ModelExplainabilityBaselineConfig: { BaseliningJobName: 'STRING_VALUE', ConstraintsResource: { S3Uri: 'STRING_VALUE' } }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createModelExplainabilityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (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.
ModelExplainabilityBaselineConfig
— (map
)The baseline configuration for a model explainability job.
BaseliningJobName
— (String
)The name of the baseline model explainability job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
ModelExplainabilityAppSpecification
— (map
)Configures the model explainability job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the model explainability job.
ConfigUri
— required — (String
)JSON formatted S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
ModelExplainabilityJobInput
— (map
)Inputs for the model explainability job.
EndpointInput
— required — (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ModelExplainabilityJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"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"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (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(s) that run the model monitoring job.
NetworkConfig
— (map
)Networking options for a model explainability job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Tags
— (Array<map>
)(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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the model explainability job.
-
(AWS.Response)
—
Returns:
createModelPackage(params = {}, callback) ⇒ AWS.Request
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 forSourceAlgorithmSpecification
.Note: 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.
Service Reference:
Examples:
Calling the createModelPackage operation
var params = { AdditionalInferenceSpecifications: [ { Containers: [ /* required */ { Image: 'STRING_VALUE', /* required */ ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Framework: 'STRING_VALUE', FrameworkVersion: 'STRING_VALUE', ImageDigest: 'STRING_VALUE', ModelDataUrl: 'STRING_VALUE', ModelInput: { DataInputConfig: 'STRING_VALUE' /* required */ }, NearestModelName: 'STRING_VALUE', ProductId: 'STRING_VALUE' }, /* more items */ ], Name: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', SupportedContentTypes: [ 'STRING_VALUE', /* more items */ ], SupportedRealtimeInferenceInstanceTypes: [ 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, /* more items */ ], SupportedResponseMIMETypes: [ 'STRING_VALUE', /* more items */ ], SupportedTransformInstanceTypes: [ 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, /* more items */ ] }, /* more items */ ], CertifyForMarketplace: true || false, ClientToken: 'STRING_VALUE', CustomerMetadataProperties: { '<CustomerMetadataKey>': 'STRING_VALUE', /* '<CustomerMetadataKey>': ... */ }, Domain: 'STRING_VALUE', DriftCheckBaselines: { Bias: { ConfigFile: { S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE', ContentType: 'STRING_VALUE' }, PostTrainingConstraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, PreTrainingConstraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, Explainability: { ConfigFile: { S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE', ContentType: 'STRING_VALUE' }, Constraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, ModelDataQuality: { Constraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, Statistics: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, ModelQuality: { Constraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, Statistics: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } } }, InferenceSpecification: { Containers: [ /* required */ { Image: 'STRING_VALUE', /* required */ ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Framework: 'STRING_VALUE', FrameworkVersion: 'STRING_VALUE', ImageDigest: 'STRING_VALUE', ModelDataUrl: 'STRING_VALUE', ModelInput: { DataInputConfig: 'STRING_VALUE' /* required */ }, NearestModelName: 'STRING_VALUE', ProductId: 'STRING_VALUE' }, /* more items */ ], SupportedContentTypes: [ /* required */ 'STRING_VALUE', /* more items */ ], SupportedResponseMIMETypes: [ /* required */ 'STRING_VALUE', /* more items */ ], SupportedRealtimeInferenceInstanceTypes: [ 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, /* more items */ ], SupportedTransformInstanceTypes: [ 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, /* more items */ ] }, MetadataProperties: { CommitId: 'STRING_VALUE', GeneratedBy: 'STRING_VALUE', ProjectId: 'STRING_VALUE', Repository: 'STRING_VALUE' }, ModelApprovalStatus: Approved | Rejected | PendingManualApproval, ModelMetrics: { Bias: { PostTrainingReport: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, PreTrainingReport: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, Report: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, Explainability: { Report: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, ModelDataQuality: { Constraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, Statistics: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, ModelQuality: { Constraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, Statistics: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } } }, ModelPackageDescription: 'STRING_VALUE', ModelPackageGroupName: 'STRING_VALUE', ModelPackageName: 'STRING_VALUE', SamplePayloadUrl: 'STRING_VALUE', SourceAlgorithmSpecification: { SourceAlgorithms: [ /* required */ { AlgorithmName: 'STRING_VALUE', /* required */ ModelDataUrl: 'STRING_VALUE' }, /* more items */ ] }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], Task: 'STRING_VALUE', ValidationSpecification: { ValidationProfiles: [ /* required */ { ProfileName: 'STRING_VALUE', /* required */ TransformJobDefinition: { /* required */ TransformInput: { /* required */ DataSource: { /* required */ S3DataSource: { /* required */ S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE' /* required */ } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', SplitType: None | Line | RecordIO | TFRecord }, TransformOutput: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ Accept: 'STRING_VALUE', AssembleWith: None | Line, KmsKeyId: 'STRING_VALUE' }, TransformResources: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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_VALUE' }, BatchStrategy: MultiRecord | SingleRecord, Environment: { '<TransformEnvironmentKey>': 'STRING_VALUE', /* '<TransformEnvironmentKey>': ... */ }, MaxConcurrentTransforms: 'NUMBER_VALUE', MaxPayloadInMB: 'NUMBER_VALUE' } }, /* more items */ ], ValidationRole: 'STRING_VALUE' /* required */ } }; sagemaker.createModelPackage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageName
— (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.
ModelPackageGroupName
— (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.
ModelPackageDescription
— (String
)A description of the model package.
InferenceSpecification
— (map
)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.
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes
— required — (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— required — (Array<String>
)The supported MIME types for the output data.
-
ValidationSpecification
— (map
)Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.
ValidationRole
— required — (String
)The IAM roles to be used for the validation of the model package.
ValidationProfiles
— required — (Array<map>
)An array of
ModelPackageValidationProfile
objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.ProfileName
— required — (String
)The name of the profile for the model package.
TransformJobDefinition
— required — (map
)The
TransformJobDefinition
object that describes the transform job used for the validation of the model package.MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB
— (Integer
)The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy
— (String
)A string that determines the number of records included in a single mini-batch.
SingleRecord
means only one record is used per mini-batch.MultiRecord
means a mini-batch is set to contain as many records that can fit within theMaxPayloadInMB
limit."MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— required — (map
)A description of the input source and the way the transform job consumes it.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— required — (map
)Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
TransformResources
— required — (map
)Identifies the ML compute instances for the transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."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"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is
1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
SourceAlgorithmSpecification
— (map
)Details about the algorithm that was used to create the model package.
SourceAlgorithms
— required — (Array<map>
)A list of the algorithms that were used to create a model package.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the algorithm.AlgorithmName
— required — (String
)The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
CertifyForMarketplace
— (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.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ModelApprovalStatus
— (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
Possible values include:Approved
to deploy the model."Approved"
"Rejected"
"PendingManualApproval"
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
ModelMetrics
— (map
)A structure that contains model metrics reports.
ModelQuality
— (map
)Metrics that measure the quality of a model.
Statistics
— (map
)Model quality statistics.
ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
Constraints
— (map
)Model quality constraints.
ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
ModelDataQuality
— (map
)Metrics that measure the quality of the input data for a model.
Statistics
— (map
)Data quality statistics for a model.
ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
Constraints
— (map
)Data quality constraints for a model.
ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
Bias
— (map
)Metrics that measure bais in a model.
Report
— (map
)The bias report for a model
ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
PreTrainingReport
— (map
)ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
PostTrainingReport
— (map
)ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
Explainability
— (map
)Metrics that help explain a model.
Report
— (map
)The explainability report for a model.
ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
ClientToken
— (String
)A unique token that guarantees that the call to this API is idempotent.
If a token is not provided, the SDK will use a version 4 UUID.CustomerMetadataProperties
— (map<String>
)The metadata properties associated with the model package versions.
DriftCheckBaselines
— (map
)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.
Bias
— (map
)Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
ConfigFile
— (map
)The bias config file for a model.
ContentType
— (String
)The type of content stored in the file source.
ContentDigest
— (String
)The digest of the file source.
S3Uri
— required — (String
)The Amazon S3 URI for the file source.
PreTrainingConstraints
— (map
)ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
PostTrainingConstraints
— (map
)ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
Explainability
— (map
)Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Constraints
— (map
)ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
ConfigFile
— (map
)The explainability config file for the model.
ContentType
— (String
)The type of content stored in the file source.
ContentDigest
— (String
)The digest of the file source.
S3Uri
— required — (String
)The Amazon S3 URI for the file source.
ModelQuality
— (map
)Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Statistics
— (map
)ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
Constraints
— (map
)ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
ModelDataQuality
— (map
)Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
Statistics
— (map
)ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
Constraints
— (map
)ContentType
— required — (String
)ContentDigest
— (String
)S3Uri
— required — (String
)
Domain
— (String
)The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
Task
— (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.
SamplePayloadUrl
— (String
)The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
AdditionalInferenceSpecifications
— (Array<map>
)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.
Name
— required — (String
)A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description
— (String
)A description of the additional Inference specification
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageArn
— (String
)The Amazon Resource Name (ARN) of the new model package.
-
(AWS.Response)
—
Returns:
createModelPackageGroup(params = {}, callback) ⇒ AWS.Request
Creates a model group. A model group contains a group of model versions.
Service Reference:
Examples:
Calling the createModelPackageGroup operation
var params = { ModelPackageGroupName: 'STRING_VALUE', /* required */ ModelPackageGroupDescription: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createModelPackageGroup(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageGroupName
— (String
)The name of the model group.
ModelPackageGroupDescription
— (String
)A description for the model group.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageGroupArn
— (String
)The Amazon Resource Name (ARN) of the model group.
-
(AWS.Response)
—
Returns:
createModelQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
Service Reference:
Examples:
Calling the createModelQualityJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE', /* required */ JobResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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 */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, ModelQualityAppSpecification: { /* required */ ImageUri: 'STRING_VALUE', /* required */ ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ }, PostAnalyticsProcessorSourceUri: 'STRING_VALUE', ProblemType: BinaryClassification | MulticlassClassification | Regression, RecordPreprocessorSourceUri: 'STRING_VALUE' }, ModelQualityJobInput: { /* required */ EndpointInput: { /* required */ EndpointName: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' }, GroundTruthS3Input: { /* required */ S3Uri: 'STRING_VALUE' } }, ModelQualityJobOutputConfig: { /* required */ MonitoringOutputs: [ /* required */ { S3Output: { /* required */ LocalPath: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ ModelQualityBaselineConfig: { BaseliningJobName: 'STRING_VALUE', ConstraintsResource: { S3Uri: 'STRING_VALUE' } }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createModelQualityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the monitoring job definition.
ModelQualityBaselineConfig
— (map
)Specifies the constraints and baselines for the monitoring job.
BaseliningJobName
— (String
)The name of the job that performs baselining for the monitoring job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
ModelQualityAppSpecification
— (map
)The container that runs the monitoring job.
ImageUri
— required — (String
)The address of the container image that the monitoring job runs.
ContainerEntrypoint
— (Array<String>
)Specifies the entrypoint for a container that the monitoring job runs.
ContainerArguments
— (Array<String>
)An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
ProblemType
— (String
)The machine learning problem type of the model that the monitoring job monitors.
Possible values include:"BinaryClassification"
"MulticlassClassification"
"Regression"
Environment
— (map<String>
)Sets the environment variables in the container that the monitoring job runs.
ModelQualityJobInput
— (map
)A list of the inputs that are monitored. Currently endpoints are supported.
EndpointInput
— required — (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
GroundTruthS3Input
— required — (map
)The ground truth label provided for the model.
S3Uri
— (String
)The address of the Amazon S3 location of the ground truth labels.
ModelQualityJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"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"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (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(s) that run the model monitoring job.
NetworkConfig
— (map
)Specifies the network configuration for the monitoring job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Tags
— (Array<map>
)(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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the model quality monitoring job.
-
(AWS.Response)
—
Returns:
createMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
Service Reference:
Examples:
Calling the createMonitoringSchedule operation
var params = { MonitoringScheduleConfig: { /* required */ MonitoringJobDefinition: { MonitoringAppSpecification: { /* required */ ImageUri: 'STRING_VALUE', /* required */ ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], PostAnalyticsProcessorSourceUri: 'STRING_VALUE', RecordPreprocessorSourceUri: 'STRING_VALUE' }, MonitoringInputs: [ /* required */ { EndpointInput: { /* required */ EndpointName: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' } }, /* more items */ ], MonitoringOutputConfig: { /* required */ MonitoringOutputs: [ /* required */ { S3Output: { /* required */ LocalPath: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, MonitoringResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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 */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, RoleArn: 'STRING_VALUE', /* required */ BaselineConfig: { BaseliningJobName: 'STRING_VALUE', ConstraintsResource: { S3Uri: 'STRING_VALUE' }, StatisticsResource: { S3Uri: 'STRING_VALUE' } }, Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ } }, MonitoringJobDefinitionName: 'STRING_VALUE', MonitoringType: DataQuality | ModelQuality | ModelBias | ModelExplainability, ScheduleConfig: { ScheduleExpression: 'STRING_VALUE' /* required */ } }, MonitoringScheduleName: 'STRING_VALUE', /* required */ Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createMonitoringSchedule(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MonitoringScheduleName
— (String
)The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
MonitoringScheduleConfig
— (map
)The configuration object that specifies the monitoring schedule and defines the monitoring job.
ScheduleConfig
— (map
)Configures the monitoring schedule.
ScheduleExpression
— required — (String
)A cron expression that describes details about the monitoring schedule.
Currently the only supported cron expressions are:
-
If you want to set the job to start every hour, please use the following:
Hourly: cron(0 * ? * * *)
-
If you want to start the job daily:
cron(0 [00-23] ? * * *)
For example, the following are valid cron expressions:
-
Daily at noon UTC:
cron(0 12 ? * * *)
-
Daily at midnight UTC:
cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
-
Every 12 hours, starting at 5pm UTC:
cron(0 17/12 ? * * *)
-
Every two hours starting at midnight:
cron(0 0/2 ? * * *)
Note:- Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
- We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
-
MonitoringJobDefinition
— (map
)Defines the monitoring job.
BaselineConfig
— (map
)Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName
— (String
)The name of the job that performs baselining for the monitoring job.
ConstraintsResource
— (map
)The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
StatisticsResource
— (map
)The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri
— (String
)The Amazon S3 URI for the statistics resource.
MonitoringInputs
— required — (Array<map>
)The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
EndpointInput
— required — (map
)The endpoint for a monitoring job.
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
MonitoringOutputConfig
— required — (map
)The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources
— required — (map
)Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"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"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (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(s) that run the model monitoring job.
MonitoringAppSpecification
— required — (map
)Configures the monitoring job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the monitoring job.
ContainerEntrypoint
— (Array<String>
)Specifies the entrypoint for a container used to run the monitoring job.
ContainerArguments
— (Array<String>
)An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition
— (map
)Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
NetworkConfig
— (map
)Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between distributed processing jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig
— (map
)Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName
— (String
)The name of the monitoring job definition to schedule.
MonitoringType
— (String
)The type of the monitoring job definition to schedule.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
Tags
— (Array<map>
)(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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:MonitoringScheduleArn
— (String
)The Amazon Resource Name (ARN) of the monitoring schedule.
-
(AWS.Response)
—
Returns:
createNotebookInstance(params = {}, callback) ⇒ AWS.Request
Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
In a
CreateNotebookInstance
request, specify the type of ML compute instance that you want to run. SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, SageMaker does the following:
-
Creates a network interface in the SageMaker VPC.
-
(Option) If you specified
SubnetId
, SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC. -
Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified
SubnetId
of your VPC, SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.
After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
Service Reference:
Examples:
Calling the createNotebookInstance operation
var params = { 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, /* required */ NotebookInstanceName: 'STRING_VALUE', /* required */ RoleArn: 'STRING_VALUE', /* required */ AcceleratorTypes: [ ml.eia1.medium | ml.eia1.large | ml.eia1.xlarge | ml.eia2.medium | ml.eia2.large | ml.eia2.xlarge, /* more items */ ], AdditionalCodeRepositories: [ 'STRING_VALUE', /* more items */ ], DefaultCodeRepository: 'STRING_VALUE', DirectInternetAccess: Enabled | Disabled, InstanceMetadataServiceConfiguration: { MinimumInstanceMetadataServiceVersion: 'STRING_VALUE' /* required */ }, KmsKeyId: 'STRING_VALUE', LifecycleConfigName: 'STRING_VALUE', PlatformIdentifier: 'STRING_VALUE', RootAccess: Enabled | Disabled, SecurityGroupIds: [ 'STRING_VALUE', /* more items */ ], SubnetId: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], VolumeSizeInGB: 'NUMBER_VALUE' }; sagemaker.createNotebookInstance(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceName
— (String
)The name of the new notebook instance.
InstanceType
— (String
)The type of ML compute instance to launch for the notebook instance.
Possible values include:"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"
SubnetId
— (String
)The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.
SecurityGroupIds
— (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
RoleArn
— (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.
Note: To be able to pass this role to SageMaker, the caller of this API must have theiam:PassRole
permission.KmsKeyId
— (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.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
LifecycleConfigName
— (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.
DirectInternetAccess
— (String
)Sets whether SageMaker provides internet access to the notebook instance. If you set this to
Disabled
this notebook instance is able to access resources only in your VPC, and is not be able to connect to SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC.For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to
Possible values include:Disabled
only if you set a value for theSubnetId
parameter."Enabled"
"Disabled"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.
AcceleratorTypes
— (Array<String>
)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.
DefaultCodeRepository
— (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.
AdditionalCodeRepositories
— (Array<String>
)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.
RootAccess
— (String
)Whether root access is enabled or disabled for users of the notebook instance. The default value is
Enabled
.Note: 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.Possible values include:"Enabled"
"Disabled"
PlatformIdentifier
— (String
)The platform identifier of the notebook instance runtime environment.
InstanceMetadataServiceConfiguration
— (map
)Information on the IMDS configuration of the notebook instance
MinimumInstanceMetadataServiceVersion
— required — (String
)Indicates the minimum IMDS version that the notebook instance supports. When passed as part of
CreateNotebookInstance
, if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part ofUpdateNotebookInstance
, there is no default.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NotebookInstanceArn
— (String
)The Amazon Resource Name (ARN) of the notebook instance.
-
(AWS.Response)
—
Returns:
createNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the createNotebookInstanceLifecycleConfig operation
var params = { NotebookInstanceLifecycleConfigName: 'STRING_VALUE', /* required */ OnCreate: [ { Content: 'STRING_VALUE' }, /* more items */ ], OnStart: [ { Content: 'STRING_VALUE' }, /* more items */ ] }; sagemaker.createNotebookInstanceLifecycleConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceLifecycleConfigName
— (String
)The name of the lifecycle configuration.
OnCreate
— (Array<map>
)A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
Content
— (String
)A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
OnStart
— (Array<map>
)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.
Content
— (String
)A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NotebookInstanceLifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the lifecycle configuration.
-
(AWS.Response)
—
Returns:
createPipeline(params = {}, callback) ⇒ AWS.Request
Creates a pipeline using a JSON pipeline definition.
Service Reference:
Examples:
Calling the createPipeline operation
var params = { ClientRequestToken: 'STRING_VALUE', /* required */ PipelineName: 'STRING_VALUE', /* required */ RoleArn: 'STRING_VALUE', /* required */ ParallelismConfiguration: { MaxParallelExecutionSteps: 'NUMBER_VALUE' /* required */ }, PipelineDefinition: 'STRING_VALUE', PipelineDefinitionS3Location: { Bucket: 'STRING_VALUE', /* required */ ObjectKey: 'STRING_VALUE', /* required */ VersionId: 'STRING_VALUE' }, PipelineDescription: 'STRING_VALUE', PipelineDisplayName: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createPipeline(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineName
— (String
)The name of the pipeline.
PipelineDisplayName
— (String
)The display name of the pipeline.
PipelineDefinition
— (String
)The JSON pipeline definition of the pipeline.
PipelineDefinitionS3Location
— (map
)The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.
Bucket
— required — (String
)Name of the S3 bucket.
ObjectKey
— required — (String
)The object key (or key name) uniquely identifies the object in an S3 bucket.
VersionId
— (String
)Version Id of the pipeline definition file. If not specified, Amazon SageMaker will retrieve the latest version.
PipelineDescription
— (String
)A description of the pipeline.
ClientRequestToken
— (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.
If a token is not provided, the SDK will use a version 4 UUID.RoleArn
— (String
)The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.
Tags
— (Array<map>
)A list of tags to apply to the created pipeline.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ParallelismConfiguration
— (map
)This is the configuration that controls the parallelism of the pipeline. If specified, it applies to all runs of this pipeline by default.
MaxParallelExecutionSteps
— required — (Integer
)The max number of steps that can be executed in parallel.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineArn
— (String
)The Amazon Resource Name (ARN) of the created pipeline.
-
(AWS.Response)
—
Returns:
createPresignedDomainUrl(params = {}, callback) ⇒ AWS.Request
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
The IAM role or user used to call this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.
You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to SageMaker Studio Through an Interface VPC Endpoint .
Note: The URL that you get from a call toCreatePresignedDomainUrl
has a default timeout of 5 minutes. You can configure this value usingExpiresInSeconds
. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.Service Reference:
Examples:
Calling the createPresignedDomainUrl operation
var params = { DomainId: 'STRING_VALUE', /* required */ UserProfileName: 'STRING_VALUE', /* required */ ExpiresInSeconds: 'NUMBER_VALUE', SessionExpirationDurationInSeconds: 'NUMBER_VALUE' }; sagemaker.createPresignedDomainUrl(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The name of the UserProfile to sign-in as.
SessionExpirationDurationInSeconds
— (Integer
)The session expiration duration in seconds. This value defaults to 43200.
ExpiresInSeconds
— (Integer
)The number of seconds until the pre-signed URL expires. This value defaults to 300.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AuthorizedUrl
— (String
)The presigned URL.
-
(AWS.Response)
—
Returns:
createPresignedNotebookInstanceUrl(params = {}, callback) ⇒ AWS.Request
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 theaws: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.Note: 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.Service Reference:
Examples:
Calling the createPresignedNotebookInstanceUrl operation
var params = { NotebookInstanceName: 'STRING_VALUE', /* required */ SessionExpirationDurationInSeconds: 'NUMBER_VALUE' }; sagemaker.createPresignedNotebookInstanceUrl(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceName
— (String
)The name of the notebook instance.
SessionExpirationDurationInSeconds
— (Integer
)The duration of the session, in seconds. The default is 12 hours.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AuthorizedUrl
— (String
)A JSON object that contains the URL string.
-
(AWS.Response)
—
Returns:
createProcessingJob(params = {}, callback) ⇒ AWS.Request
Creates a processing job.
Service Reference:
Examples:
Calling the createProcessingJob operation
var params = { AppSpecification: { /* required */ ImageUri: 'STRING_VALUE', /* required */ ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ] }, ProcessingJobName: 'STRING_VALUE', /* required */ ProcessingResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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 */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, RoleArn: 'STRING_VALUE', /* required */ Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ }, ExperimentConfig: { ExperimentName: 'STRING_VALUE', TrialComponentDisplayName: 'STRING_VALUE', TrialName: 'STRING_VALUE' }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, ProcessingInputs: [ { InputName: 'STRING_VALUE', /* required */ AppManaged: true || false, DatasetDefinition: { AthenaDatasetDefinition: { Catalog: 'STRING_VALUE', /* required */ Database: 'STRING_VALUE', /* required */ OutputFormat: PARQUET | ORC | AVRO | JSON | TEXTFILE, /* required */ OutputS3Uri: 'STRING_VALUE', /* required */ QueryString: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', OutputCompression: GZIP | SNAPPY | ZLIB, WorkGroup: 'STRING_VALUE' }, DataDistributionType: FullyReplicated | ShardedByS3Key, InputMode: Pipe | File, LocalPath: 'STRING_VALUE', RedshiftDatasetDefinition: { ClusterId: 'STRING_VALUE', /* required */ ClusterRoleArn: 'STRING_VALUE', /* required */ Database: 'STRING_VALUE', /* required */ DbUser: 'STRING_VALUE', /* required */ OutputFormat: PARQUET | CSV, /* required */ OutputS3Uri: 'STRING_VALUE', /* required */ QueryString: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', OutputCompression: None | GZIP | BZIP2 | ZSTD | SNAPPY } }, S3Input: { S3DataType: ManifestFile | S3Prefix, /* required */ S3Uri: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', S3CompressionType: None | Gzip, S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File } }, /* more items */ ], ProcessingOutputConfig: { Outputs: [ /* required */ { OutputName: 'STRING_VALUE', /* required */ AppManaged: true || false, FeatureStoreOutput: { FeatureGroupName: 'STRING_VALUE' /* required */ }, S3Output: { LocalPath: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob, /* required */ S3Uri: 'STRING_VALUE' /* required */ } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createProcessingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ProcessingInputs
— (Array<map>
)An array of inputs configuring the data to download into the processing container.
InputName
— required — (String
)The name for the processing job input.
AppManaged
— (Boolean
)When
True
, input operations such as data download are managed natively by the processing job application. WhenFalse
(default), input operations are managed by Amazon SageMaker.S3Input
— (map
)Configuration for downloading input data from Amazon S3 into the processing container.
S3Uri
— required — (String
)The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
LocalPath
— (String
)The local path in your container where you want Amazon SageMaker to write input data to.
LocalPath
is an absolute path to the input data and must begin with/opt/ml/processing/
.LocalPath
is a required parameter whenAppManaged
isFalse
(default).S3DataType
— required — (String
)Whether you use an
Possible values include:S3Prefix
or aManifestFile
for the data type. If you chooseS3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you chooseManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job."ManifestFile"
"S3Prefix"
S3InputMode
— (String
)Whether to use
Possible values include:File
orPipe
input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. InPipe
mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume."Pipe"
"File"
S3DataDistributionType
— (String
)Whether to distribute the data from Amazon S3 to all processing instances with
Possible values include:FullyReplicated
, or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance."FullyReplicated"
"ShardedByS3Key"
S3CompressionType
— (String
)Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container.
Possible values include:Gzip
can only be used whenPipe
mode is specified as theS3InputMode
. InPipe
mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume."None"
"Gzip"
DatasetDefinition
— (map
)Configuration for a Dataset Definition input.
AthenaDatasetDefinition
— (map
)Configuration for Athena Dataset Definition input.
Catalog
— required — (String
)The name of the data catalog used in Athena query execution.
Database
— required — (String
)The name of the database used in the Athena query execution.
QueryString
— required — (String
)The SQL query statements, to be executed.
WorkGroup
— (String
)The name of the workgroup in which the Athena query is being started.
OutputS3Uri
— required — (String
)The location in Amazon S3 where Athena query results are stored.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat
— required — (String
)The data storage format for Athena query results.
Possible values include:"PARQUET"
"ORC"
"AVRO"
"JSON"
"TEXTFILE"
OutputCompression
— (String
)The compression used for Athena query results.
Possible values include:"GZIP"
"SNAPPY"
"ZLIB"
RedshiftDatasetDefinition
— (map
)Configuration for Redshift Dataset Definition input.
ClusterId
— required — (String
)The Redshift cluster Identifier.
Database
— required — (String
)The name of the Redshift database used in Redshift query execution.
DbUser
— required — (String
)The database user name used in Redshift query execution.
QueryString
— required — (String
)The SQL query statements to be executed.
ClusterRoleArn
— required — (String
)The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri
— required — (String
)The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat
— required — (String
)The data storage format for Redshift query results.
Possible values include:"PARQUET"
"CSV"
OutputCompression
— (String
)The compression used for Redshift query results.
Possible values include:"None"
"GZIP"
"BZIP2"
"ZSTD"
"SNAPPY"
LocalPath
— (String
)The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job.
LocalPath
is an absolute path to the input data. This is a required parameter whenAppManaged
isFalse
(default).DataDistributionType
— (String
)Whether the generated dataset is
Possible values include:FullyReplicated
orShardedByS3Key
(default)."FullyReplicated"
"ShardedByS3Key"
InputMode
— (String
)Whether to use
Possible values include:File
orPipe
input mode. InFile
(default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. InPipe
mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume."Pipe"
"File"
ProcessingOutputConfig
— (map
)Output configuration for the processing job.
Outputs
— required — (Array<map>
)An array of outputs configuring the data to upload from the processing container.
OutputName
— required — (String
)The name for the processing job output.
S3Output
— (map
)Configuration for processing job outputs in Amazon S3.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath
— required — (String
)The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3.
LocalPath
is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.S3UploadMode
— required — (String
)Whether to upload the results of the processing job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
FeatureStoreOutput
— (map
)Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when
AppManaged
is specified.FeatureGroupName
— required — (String
)The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
AppManaged
— (Boolean
)When
True
, output operations such as data upload are managed natively by the processing job application. WhenFalse
(default), output operations are managed by Amazon SageMaker.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output.
KmsKeyId
can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. TheKmsKeyId
is applied to all outputs.
ProcessingJobName
— (String
)The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
ProcessingResources
— (map
)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.
ClusterConfig
— required — (map
)The configuration for the resources in a cluster used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"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"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note: Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request aVolumeSizeInGB
greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.VolumeKmsKeyId
— (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(s) that run the processing job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.
StoppingCondition
— (map
)The time limit for how long the processing job is allowed to run.
MaxRuntimeInSeconds
— required — (Integer
)Specifies the maximum runtime in seconds.
AppSpecification
— (map
)Configures the processing job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the processing job.
ContainerEntrypoint
— (Array<String>
)The entrypoint for a container used to run a processing job.
ContainerArguments
— (Array<String>
)The arguments for a container used to run a processing job.
Environment
— (map<String>
)The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.
NetworkConfig
— (map
)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.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between distributed processing jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig
— (map
)Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
Tags
— (Array<map>
)(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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate the trial component with.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ProcessingJobArn
— (String
)The Amazon Resource Name (ARN) of the processing job.
-
(AWS.Response)
—
Returns:
createProject(params = {}, callback) ⇒ AWS.Request
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.
Service Reference:
Examples:
Calling the createProject operation
var params = { ProjectName: 'STRING_VALUE', /* required */ ServiceCatalogProvisioningDetails: { /* required */ ProductId: 'STRING_VALUE', /* required */ PathId: 'STRING_VALUE', ProvisioningArtifactId: 'STRING_VALUE', ProvisioningParameters: [ { Key: 'STRING_VALUE', Value: 'STRING_VALUE' }, /* more items */ ] }, ProjectDescription: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createProject(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ProjectName
— (String
)The name of the project.
ProjectDescription
— (String
)A description for the project.
ServiceCatalogProvisioningDetails
— (map
)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.
ProductId
— required — (String
)The ID of the product to provision.
ProvisioningArtifactId
— (String
)The ID of the provisioning artifact.
PathId
— (String
)The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
ProvisioningParameters
— (Array<map>
)A list of key value pairs that you specify when you provision a product.
Key
— (String
)The key that identifies a provisioning parameter.
Value
— (String
)The value of the provisioning parameter.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ProjectArn
— (String
)The Amazon Resource Name (ARN) of the project.
ProjectId
— (String
)The ID of the new project.
-
(AWS.Response)
—
Returns:
createStudioLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Creates a new Studio Lifecycle Configuration.
Service Reference:
Examples:
Calling the createStudioLifecycleConfig operation
var params = { StudioLifecycleConfigAppType: JupyterServer | KernelGateway, /* required */ StudioLifecycleConfigContent: 'STRING_VALUE', /* required */ StudioLifecycleConfigName: 'STRING_VALUE', /* required */ Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createStudioLifecycleConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
StudioLifecycleConfigName
— (String
)The name of the Studio Lifecycle Configuration to create.
StudioLifecycleConfigContent
— (String
)The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
StudioLifecycleConfigAppType
— (String
)The App type that the Lifecycle Configuration is attached to.
Possible values include:"JupyterServer"
"KernelGateway"
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:StudioLifecycleConfigArn
— (String
)The ARN of your created Lifecycle Configuration.
-
(AWS.Response)
—
Returns:
createTrainingJob(params = {}, callback) ⇒ AWS.Request
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. -
InputDataConfig
- Describes the training dataset 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, useMaxRuntimeInSeconds
to set a time limit for training. UseMaxWaitTimeInSeconds
to specify how long a managed spot training job has to complete. -
Environment
- The environment variables to set in the Docker container. -
RetryStrategy
- The number of times to retry the job when the job fails due to anInternalServerError
.
For more information about SageMaker, see How It Works.
Service Reference:
Examples:
Calling the createTrainingJob operation
var params = { AlgorithmSpecification: { /* required */ TrainingInputMode: Pipe | File | FastFile, /* required */ AlgorithmName: 'STRING_VALUE', EnableSageMakerMetricsTimeSeries: true || false, MetricDefinitions: [ { Name: 'STRING_VALUE', /* required */ Regex: 'STRING_VALUE' /* required */ }, /* more items */ ], TrainingImage: 'STRING_VALUE' }, OutputDataConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, ResourceConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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, /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ StoppingCondition: { /* required */ MaxRuntimeInSeconds: 'NUMBER_VALUE', MaxWaitTimeInSeconds: 'NUMBER_VALUE' }, TrainingJobName: 'STRING_VALUE', /* required */ CheckpointConfig: { S3Uri: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE' }, DebugHookConfig: { S3OutputPath: 'STRING_VALUE', /* required */ CollectionConfigurations: [ { CollectionName: 'STRING_VALUE', CollectionParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ } }, /* more items */ ], HookParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ }, LocalPath: 'STRING_VALUE' }, DebugRuleConfigurations: [ { RuleConfigurationName: 'STRING_VALUE', /* required */ RuleEvaluatorImage: 'STRING_VALUE', /* 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, LocalPath: 'STRING_VALUE', RuleParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ }, S3OutputPath: 'STRING_VALUE', VolumeSizeInGB: 'NUMBER_VALUE' }, /* more items */ ], EnableInterContainerTrafficEncryption: true || false, EnableManagedSpotTraining: true || false, EnableNetworkIsolation: true || false, Environment: { '<TrainingEnvironmentKey>': 'STRING_VALUE', /* '<TrainingEnvironmentKey>': ... */ }, ExperimentConfig: { ExperimentName: 'STRING_VALUE', TrialComponentDisplayName: 'STRING_VALUE', TrialName: 'STRING_VALUE' }, HyperParameters: { '<HyperParameterKey>': 'STRING_VALUE', /* '<HyperParameterKey>': ... */ }, InputDataConfig: [ { ChannelName: 'STRING_VALUE', /* required */ DataSource: { /* required */ FileSystemDataSource: { DirectoryPath: 'STRING_VALUE', /* required */ FileSystemAccessMode: rw | ro, /* required */ FileSystemId: 'STRING_VALUE', /* required */ FileSystemType: EFS | FSxLustre /* required */ }, S3DataSource: { S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE', /* required */ AttributeNames: [ 'STRING_VALUE', /* more items */ ], S3DataDistributionType: FullyReplicated | ShardedByS3Key } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', InputMode: Pipe | File | FastFile, RecordWrapperType: None | RecordIO, ShuffleConfig: { Seed: 'NUMBER_VALUE' /* required */ } }, /* more items */ ], ProfilerConfig: { S3OutputPath: 'STRING_VALUE', /* required */ ProfilingIntervalInMilliseconds: 'NUMBER_VALUE', ProfilingParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ } }, ProfilerRuleConfigurations: [ { RuleConfigurationName: 'STRING_VALUE', /* required */ RuleEvaluatorImage: 'STRING_VALUE', /* 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, LocalPath: 'STRING_VALUE', RuleParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ }, S3OutputPath: 'STRING_VALUE', VolumeSizeInGB: 'NUMBER_VALUE' }, /* more items */ ], RetryStrategy: { MaximumRetryAttempts: 'NUMBER_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], TensorBoardOutputConfig: { S3OutputPath: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE' }, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }; sagemaker.createTrainingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrainingJobName
— (String
)The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
HyperParameters
— (map<String>
)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
.AlgorithmSpecification
— (map
)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.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. For more information, see the note in theAlgorithmName
parameter description.AlgorithmName
— (String
)The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. Note that theAlgorithmName
parameter is mutually exclusive with theTrainingImage
parameter. If you specify a value for theAlgorithmName
parameter, you can't specify a value forTrainingImage
, and vice versa. If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise anull
error.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
MetricDefinitions
— (Array<map>
)A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics.
EnableSageMakerMetricsTimeSeries
— (Boolean
)To generate and save time-series metrics during training, set to
true
. The default isfalse
and time-series metrics aren't generated except in the following cases:-
You use one of the SageMaker built-in algorithms
-
You use one of the following Prebuilt SageMaker Docker Images:
-
Tensorflow (version >= 1.15)
-
MXNet (version >= 1.6)
-
PyTorch (version >= 1.3)
-
-
You specify at least one MetricDefinition
-
RoleArn
— (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.
Note: To be able to pass this role to SageMaker, the caller of this API must have theiam:PassRole
permission.InputDataConfig
— (Array<map>
)An array of
Channel
objects. Each channel is a named input source.InputDataConfig
describes the input data and its location.Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data,
training_data
andvalidation_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.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
OutputDataConfig
— (map
)Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys forOutputDataConfig
. If you use a bucket policy with ans3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.The KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.
ResourceConfig
— (map
)The resources, including the ML compute instances and ML storage volumes, to use for model training.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.InstanceType
— required — (String
)The ML compute instance type.
Possible values include:"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"
InstanceCount
— required — (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.You must specify sufficient ML storage for your scenario.
Note: SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.Note: Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request aVolumeSizeInGB
greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
VpcConfig
— (map
)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.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the
Subnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
StoppingCondition
— (map
)Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the
SIGTERM
signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.
Tags
— (Array<map>
)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.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
EnableNetworkIsolation
— (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.
EnableInterContainerTrafficEncryption
— (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
— (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.
CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
DebugHookConfig
— (map
)Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the
DebugHookConfig
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.LocalPath
— (String
)Path to local storage location for metrics and tensors. Defaults to
/opt/ml/output/tensors/
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for metrics and tensors.
HookParameters
— (map<String>
)Configuration information for the Debugger hook parameters.
CollectionConfigurations
— (Array<map>
)Configuration information for Debugger tensor collections. To learn more about how to configure the
CollectionConfiguration
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.CollectionName
— (String
)The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters
— (map<String>
)Parameter values for the tensor collection. The allowed parameters are
"name"
,"include_regex"
,"reduction_config"
,"save_config"
,"tensor_names"
, and"save_histogram"
.
DebugRuleConfigurations
— (Array<map>
)Configuration information for Debugger rules for debugging output tensors.
RuleConfigurationName
— required — (String
)The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath
— (String
)Path to local storage location for output of rules. Defaults to
/opt/ml/processing/output/rule/
.S3OutputPath
— (String
)Path to Amazon S3 storage location for rules.
RuleEvaluatorImage
— required — (String
)The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType
— (String
)The instance type to deploy a Debugger custom rule for debugging a training job.
Possible values include:"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"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters
— (map<String>
)Runtime configuration for rule container.
TensorBoardOutputConfig
— (map
)Configuration of storage locations for the Debugger TensorBoard output data.
LocalPath
— (String
)Path to local storage location for tensorBoard output. Defaults to
/opt/ml/output/tensorboard
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for TensorBoard output.
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate the trial component with.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
ProfilerConfig
— (map
)Configuration information for Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath
— required — (String
)Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds
— (Integer
)A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters
— (map<String>
)Configuration information for capturing framework metrics. Available key strings for different profiling options are
DetailedProfilingConfig
,PythonProfilingConfig
, andDataLoaderProfilingConfig
. The following codes are configuration structures for theProfilingParameters
parameter. To learn more about how to configure theProfilingParameters
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
ProfilerRuleConfigurations
— (Array<map>
)Configuration information for Debugger rules for profiling system and framework metrics.
RuleConfigurationName
— required — (String
)The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath
— (String
)Path to local storage location for output of rules. Defaults to
/opt/ml/processing/output/rule/
.S3OutputPath
— (String
)Path to Amazon S3 storage location for rules.
RuleEvaluatorImage
— required — (String
)The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType
— (String
)The instance type to deploy a Debugger custom rule for profiling a training job.
Possible values include:"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"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters
— (map<String>
)Runtime configuration for rule container.
Environment
— (map<String>
)The environment variables to set in the Docker container.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrainingJobArn
— (String
)The Amazon Resource Name (ARN) of the training job.
-
(AWS.Response)
—
Returns:
createTransformJob(params = {}, callback) ⇒ AWS.Request
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
To perform batch transformations, you create a transform job and use the data that you have readily available.
In the request body, you provide the following:
-
TransformJobName
- Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. -
ModelName
- Identifies the model to use.ModelName
must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel. -
TransformInput
- Describes the dataset to be transformed and the Amazon S3 location where it is stored. -
TransformOutput
- Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. -
TransformResources
- Identifies the ML compute instances for the transform job.
For more information about how batch transformation works, see Batch Transform.
Service Reference:
Examples:
Calling the createTransformJob operation
var params = { ModelName: 'STRING_VALUE', /* required */ TransformInput: { /* required */ DataSource: { /* required */ S3DataSource: { /* required */ S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE' /* required */ } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', SplitType: None | Line | RecordIO | TFRecord }, TransformJobName: 'STRING_VALUE', /* required */ TransformOutput: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ Accept: 'STRING_VALUE', AssembleWith: None | Line, KmsKeyId: 'STRING_VALUE' }, TransformResources: { /* required */ InstanceCount: 'NUMBER_VALUE', /* 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_VALUE' }, BatchStrategy: MultiRecord | SingleRecord, DataProcessing: { InputFilter: 'STRING_VALUE', JoinSource: Input | None, OutputFilter: 'STRING_VALUE' }, Environment: { '<TransformEnvironmentKey>': 'STRING_VALUE', /* '<TransformEnvironmentKey>': ... */ }, ExperimentConfig: { ExperimentName: 'STRING_VALUE', TrialComponentDisplayName: 'STRING_VALUE', TrialName: 'STRING_VALUE' }, MaxConcurrentTransforms: 'NUMBER_VALUE', MaxPayloadInMB: 'NUMBER_VALUE', ModelClientConfig: { InvocationsMaxRetries: 'NUMBER_VALUE', InvocationsTimeoutInSeconds: 'NUMBER_VALUE' }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createTransformJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TransformJobName
— (String
)The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
ModelName
— (String
)The name of the model that you want to use for the transform job.
ModelName
must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests that can be sent to each instance in a transform job. If
MaxConcurrentTransforms
is set to0
or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value 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
.ModelClientConfig
— (map
)Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds
— (Integer
)The timeout value in seconds for an invocation request. The default value is 600.
InvocationsMaxRetries
— (Integer
)The maximum number of retries when invocation requests are failing. The default value is 3.
MaxPayloadInMB
— (Integer
)The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in
MaxPayloadInMB
must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is6
MB.The value of
MaxPayloadInMB
cannot be greater than 100 MB. If you specify theMaxConcurrentTransforms
parameter, the value of(MaxConcurrentTransforms * MaxPayloadInMB)
also cannot exceed 100 MB.For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to
0
. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.BatchStrategy
— (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
SplitType
property toLine
,RecordIO
, orTFRecord
.To use only one record when making an HTTP invocation request to a container, set
BatchStrategy
toSingleRecord
andSplitType
toLine
.To fit as many records in a mini-batch as can fit within the
Possible values include:MaxPayloadInMB
limit, setBatchStrategy
toMultiRecord
andSplitType
toLine
."MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— (map
)Describes the input source and the way the transform job consumes it.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix
. -
A manifest might look like this:
s3://bucketname/exa
-
- listTrainingJobsForHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request