@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public interface AmazonSageMakerAsync extends AmazonSageMaker
AsyncHandler
can be used to receive
notification when an asynchronous operation completes.
Note: Do not directly implement this interface, new methods are added to it regularly. Extend from
AbstractAmazonSageMakerAsync
instead.
Provides APIs for creating and managing Amazon SageMaker resources.
Other Resources:
ENDPOINT_PREFIX
addAssociation, addTags, associateTrialComponent, createAction, createAlgorithm, createApp, createAppImageConfig, createArtifact, createAutoMLJob, createCodeRepository, createCompilationJob, createContext, createDataQualityJobDefinition, createDeviceFleet, createDomain, createEdgePackagingJob, createEndpoint, createEndpointConfig, createExperiment, createFeatureGroup, createFlowDefinition, createHumanTaskUi, createHyperParameterTuningJob, createImage, createImageVersion, createLabelingJob, createModel, createModelBiasJobDefinition, createModelExplainabilityJobDefinition, createModelPackage, createModelPackageGroup, createModelQualityJobDefinition, createMonitoringSchedule, createNotebookInstance, createNotebookInstanceLifecycleConfig, createPipeline, createPresignedDomainUrl, createPresignedNotebookInstanceUrl, createProcessingJob, createProject, createTrainingJob, createTransformJob, createTrial, createTrialComponent, createUserProfile, createWorkforce, createWorkteam, deleteAction, deleteAlgorithm, deleteApp, deleteAppImageConfig, deleteArtifact, deleteAssociation, deleteCodeRepository, deleteContext, deleteDataQualityJobDefinition, deleteDeviceFleet, deleteDomain, deleteEndpoint, deleteEndpointConfig, deleteExperiment, deleteFeatureGroup, deleteFlowDefinition, deleteHumanTaskUi, deleteImage, deleteImageVersion, deleteModel, deleteModelBiasJobDefinition, deleteModelExplainabilityJobDefinition, deleteModelPackage, deleteModelPackageGroup, deleteModelPackageGroupPolicy, deleteModelQualityJobDefinition, deleteMonitoringSchedule, deleteNotebookInstance, deleteNotebookInstanceLifecycleConfig, deletePipeline, deleteProject, deleteTags, deleteTrial, deleteTrialComponent, deleteUserProfile, deleteWorkforce, deleteWorkteam, deregisterDevices, describeAction, describeAlgorithm, describeApp, describeAppImageConfig, describeArtifact, describeAutoMLJob, describeCodeRepository, describeCompilationJob, describeContext, describeDataQualityJobDefinition, describeDevice, describeDeviceFleet, describeDomain, describeEdgePackagingJob, describeEndpoint, describeEndpointConfig, describeExperiment, describeFeatureGroup, describeFlowDefinition, describeHumanTaskUi, describeHyperParameterTuningJob, describeImage, describeImageVersion, describeLabelingJob, describeModel, describeModelBiasJobDefinition, describeModelExplainabilityJobDefinition, describeModelPackage, describeModelPackageGroup, describeModelQualityJobDefinition, describeMonitoringSchedule, describeNotebookInstance, describeNotebookInstanceLifecycleConfig, describePipeline, describePipelineDefinitionForExecution, describePipelineExecution, describeProcessingJob, describeProject, describeSubscribedWorkteam, describeTrainingJob, describeTransformJob, describeTrial, describeTrialComponent, describeUserProfile, describeWorkforce, describeWorkteam, disableSagemakerServicecatalogPortfolio, disassociateTrialComponent, enableSagemakerServicecatalogPortfolio, getCachedResponseMetadata, getDeviceFleetReport, getModelPackageGroupPolicy, getSagemakerServicecatalogPortfolioStatus, getSearchSuggestions, listActions, listAlgorithms, listAppImageConfigs, listApps, listArtifacts, listAssociations, listAutoMLJobs, listCandidatesForAutoMLJob, listCodeRepositories, listCompilationJobs, listContexts, listDataQualityJobDefinitions, listDeviceFleets, listDevices, listDomains, listEdgePackagingJobs, listEndpointConfigs, listEndpoints, listExperiments, listFeatureGroups, listFlowDefinitions, listHumanTaskUis, listHyperParameterTuningJobs, listImages, listImageVersions, listLabelingJobs, listLabelingJobsForWorkteam, listModelBiasJobDefinitions, listModelExplainabilityJobDefinitions, listModelPackageGroups, listModelPackages, listModelQualityJobDefinitions, listModels, listMonitoringExecutions, listMonitoringSchedules, listNotebookInstanceLifecycleConfigs, listNotebookInstances, listPipelineExecutions, listPipelineExecutionSteps, listPipelineParametersForExecution, listPipelines, listProcessingJobs, listProjects, listSubscribedWorkteams, listTags, listTrainingJobs, listTrainingJobsForHyperParameterTuningJob, listTransformJobs, listTrialComponents, listTrials, listUserProfiles, listWorkforces, listWorkteams, putModelPackageGroupPolicy, registerDevices, renderUiTemplate, search, shutdown, startMonitoringSchedule, startNotebookInstance, startPipelineExecution, stopAutoMLJob, stopCompilationJob, stopEdgePackagingJob, stopHyperParameterTuningJob, stopLabelingJob, stopMonitoringSchedule, stopNotebookInstance, stopPipelineExecution, stopProcessingJob, stopTrainingJob, stopTransformJob, updateAction, updateAppImageConfig, updateArtifact, updateCodeRepository, updateContext, updateDeviceFleet, updateDevices, updateDomain, updateEndpoint, updateEndpointWeightsAndCapacities, updateExperiment, updateImage, updateModelPackage, updateMonitoringSchedule, updateNotebookInstance, updateNotebookInstanceLifecycleConfig, updatePipeline, updatePipelineExecution, updateTrainingJob, updateTrial, updateTrialComponent, updateUserProfile, updateWorkforce, updateWorkteam, waiters
Future<AddAssociationResult> addAssociationAsync(AddAssociationRequest addAssociationRequest)
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.
addAssociationRequest
- Future<AddAssociationResult> addAssociationAsync(AddAssociationRequest addAssociationRequest, AsyncHandler<AddAssociationRequest,AddAssociationResult> asyncHandler)
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.
addAssociationRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<AddTagsResult> addTagsAsync(AddTagsRequest addTagsRequest)
Adds or overwrites one or more tags for the specified Amazon 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 AWS Tagging Strategies.
Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the
hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter
tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter
tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you
first create the tuning job by specifying them in the Tags
parameter of
CreateHyperParameterTuningJob
addTagsRequest
- Future<AddTagsResult> addTagsAsync(AddTagsRequest addTagsRequest, AsyncHandler<AddTagsRequest,AddTagsResult> asyncHandler)
Adds or overwrites one or more tags for the specified Amazon 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 AWS Tagging Strategies.
Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the
hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter
tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter
tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you
first create the tuning job by specifying them in the Tags
parameter of
CreateHyperParameterTuningJob
addTagsRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<AssociateTrialComponentResult> associateTrialComponentAsync(AssociateTrialComponentRequest associateTrialComponentRequest)
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.
associateTrialComponentRequest
- Future<AssociateTrialComponentResult> associateTrialComponentAsync(AssociateTrialComponentRequest associateTrialComponentRequest, AsyncHandler<AssociateTrialComponentRequest,AssociateTrialComponentResult> asyncHandler)
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.
associateTrialComponentRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateActionResult> createActionAsync(CreateActionRequest createActionRequest)
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.
createActionRequest
- Future<CreateActionResult> createActionAsync(CreateActionRequest createActionRequest, AsyncHandler<CreateActionRequest,CreateActionResult> asyncHandler)
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.
createActionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateAlgorithmResult> createAlgorithmAsync(CreateAlgorithmRequest createAlgorithmRequest)
Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
createAlgorithmRequest
- Future<CreateAlgorithmResult> createAlgorithmAsync(CreateAlgorithmRequest createAlgorithmRequest, AsyncHandler<CreateAlgorithmRequest,CreateAlgorithmResult> asyncHandler)
Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
createAlgorithmRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateAppResult> createAppAsync(CreateAppRequest createAppRequest)
Creates a running app for the specified UserProfile. Supported apps are JupyterServer
and
KernelGateway
. 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.
createAppRequest
- Future<CreateAppResult> createAppAsync(CreateAppRequest createAppRequest, AsyncHandler<CreateAppRequest,CreateAppResult> asyncHandler)
Creates a running app for the specified UserProfile. Supported apps are JupyterServer
and
KernelGateway
. 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.
createAppRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateAppImageConfigResult> createAppImageConfigAsync(CreateAppImageConfigRequest createAppImageConfigRequest)
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.
createAppImageConfigRequest
- Future<CreateAppImageConfigResult> createAppImageConfigAsync(CreateAppImageConfigRequest createAppImageConfigRequest, AsyncHandler<CreateAppImageConfigRequest,CreateAppImageConfigResult> asyncHandler)
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.
createAppImageConfigRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateArtifactResult> createArtifactAsync(CreateArtifactRequest createArtifactRequest)
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.
createArtifactRequest
- Future<CreateArtifactResult> createArtifactAsync(CreateArtifactRequest createArtifactRequest, AsyncHandler<CreateArtifactRequest,CreateArtifactResult> asyncHandler)
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.
createArtifactRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateAutoMLJobResult> createAutoMLJobAsync(CreateAutoMLJobRequest createAutoMLJobRequest)
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.
createAutoMLJobRequest
- Future<CreateAutoMLJobResult> createAutoMLJobAsync(CreateAutoMLJobRequest createAutoMLJobRequest, AsyncHandler<CreateAutoMLJobRequest,CreateAutoMLJobResult> asyncHandler)
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.
createAutoMLJobRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateCodeRepositoryResult> createCodeRepositoryAsync(CreateCodeRepositoryRequest createCodeRepositoryRequest)
Creates a Git repository as a resource in your Amazon 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 Amazon 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 AWS CodeCommit or in any other Git repository.
createCodeRepositoryRequest
- Future<CreateCodeRepositoryResult> createCodeRepositoryAsync(CreateCodeRepositoryRequest createCodeRepositoryRequest, AsyncHandler<CreateCodeRepositoryRequest,CreateCodeRepositoryResult> asyncHandler)
Creates a Git repository as a resource in your Amazon 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 Amazon 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 AWS CodeCommit or in any other Git repository.
createCodeRepositoryRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateCompilationJobResult> createCompilationJobAsync(CreateCompilationJobRequest createCompilationJobRequest)
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 AWS IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
You can also provide a Tag
to track the model compilation job's resource use and costs. The response
body contains the CompilationJobArn
for the compiled job.
To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
createCompilationJobRequest
- Future<CreateCompilationJobResult> createCompilationJobAsync(CreateCompilationJobRequest createCompilationJobRequest, AsyncHandler<CreateCompilationJobRequest,CreateCompilationJobResult> asyncHandler)
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 AWS IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
You can also provide a Tag
to track the model compilation job's resource use and costs. The response
body contains the CompilationJobArn
for the compiled job.
To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
createCompilationJobRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateContextResult> createContextAsync(CreateContextRequest createContextRequest)
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.
createContextRequest
- Future<CreateContextResult> createContextAsync(CreateContextRequest createContextRequest, AsyncHandler<CreateContextRequest,CreateContextResult> asyncHandler)
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.
createContextRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateDataQualityJobDefinitionResult> createDataQualityJobDefinitionAsync(CreateDataQualityJobDefinitionRequest createDataQualityJobDefinitionRequest)
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
createDataQualityJobDefinitionRequest
- Future<CreateDataQualityJobDefinitionResult> createDataQualityJobDefinitionAsync(CreateDataQualityJobDefinitionRequest createDataQualityJobDefinitionRequest, AsyncHandler<CreateDataQualityJobDefinitionRequest,CreateDataQualityJobDefinitionResult> asyncHandler)
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
createDataQualityJobDefinitionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateDeviceFleetResult> createDeviceFleetAsync(CreateDeviceFleetRequest createDeviceFleetRequest)
Creates a device fleet.
createDeviceFleetRequest
- Future<CreateDeviceFleetResult> createDeviceFleetAsync(CreateDeviceFleetRequest createDeviceFleetRequest, AsyncHandler<CreateDeviceFleetRequest,CreateDeviceFleetResult> asyncHandler)
Creates a device fleet.
createDeviceFleetRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateDomainResult> createDomainAsync(CreateDomainRequest createDomainRequest)
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 AWS 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 AWS Key Management Service (AWS KMS) to encrypt the EFS volume attached to the domain with an AWS managed customer master key (CMK) by default. For more control, you can specify a customer managed CMK. 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.
For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
createDomainRequest
- Future<CreateDomainResult> createDomainAsync(CreateDomainRequest createDomainRequest, AsyncHandler<CreateDomainRequest,CreateDomainResult> asyncHandler)
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 AWS 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 AWS Key Management Service (AWS KMS) to encrypt the EFS volume attached to the domain with an AWS managed customer master key (CMK) by default. For more control, you can specify a customer managed CMK. 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.
For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
createDomainRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateEdgePackagingJobResult> createEdgePackagingJobAsync(CreateEdgePackagingJobRequest createEdgePackagingJobRequest)
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.
createEdgePackagingJobRequest
- Future<CreateEdgePackagingJobResult> createEdgePackagingJobAsync(CreateEdgePackagingJobRequest createEdgePackagingJobRequest, AsyncHandler<CreateEdgePackagingJobRequest,CreateEdgePackagingJobResult> asyncHandler)
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.
createEdgePackagingJobRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateEndpointResult> createEndpointAsync(CreateEndpointRequest createEndpointRequest)
Creates an endpoint using the endpoint configuration specified in the request. Amazon 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 Amazon SageMaker hosting services.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
You must not delete an EndpointConfig
that is in use by an endpoint that is live or while the
UpdateEndpoint
or CreateEndpoint
operations are being performed on the endpoint. To
update an endpoint, you must create a new EndpointConfig
.
The endpoint name must be unique within an AWS Region in your AWS account.
When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
exists. When you read data from a DynamoDB table supporting
Eventually Consistent Reads
, the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
this causes a validation error. If you repeat your read request after a short time, the response should return
the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
DynamoDB eventually consistent read.
When Amazon SageMaker receives the request, it sets the endpoint status to Creating
. After it
creates the endpoint, it sets the status to InService
. Amazon 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, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.
Option 1: For a full Amazon 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 Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference.
createEndpointRequest
- Future<CreateEndpointResult> createEndpointAsync(CreateEndpointRequest createEndpointRequest, AsyncHandler<CreateEndpointRequest,CreateEndpointResult> asyncHandler)
Creates an endpoint using the endpoint configuration specified in the request. Amazon 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 Amazon SageMaker hosting services.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
You must not delete an EndpointConfig
that is in use by an endpoint that is live or while the
UpdateEndpoint
or CreateEndpoint
operations are being performed on the endpoint. To
update an endpoint, you must create a new EndpointConfig
.
The endpoint name must be unique within an AWS Region in your AWS account.
When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
exists. When you read data from a DynamoDB table supporting
Eventually Consistent Reads
, the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
this causes a validation error. If you repeat your read request after a short time, the response should return
the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
DynamoDB eventually consistent read.
When Amazon SageMaker receives the request, it sets the endpoint status to Creating
. After it
creates the endpoint, it sets the status to InService
. Amazon 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, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.
Option 1: For a full Amazon 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 Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference.
createEndpointRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateEndpointConfigResult> createEndpointConfigAsync(CreateEndpointConfigRequest createEndpointConfigRequest)
Creates an endpoint configuration that Amazon 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 Amazon SageMaker to provision. Then you call the CreateEndpoint API.
Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.
In the request, you define a ProductionVariant
, for each model that you want to deploy. Each
ProductionVariant
parameter also describes the resources that you want Amazon 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. Amazon SageMaker distributes two-thirds of the traffic to Model
A, and one-third to model B.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
exists. When you read data from a DynamoDB table supporting
Eventually Consistent Reads
, the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
this causes a validation error. If you repeat your read request after a short time, the response should return
the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
DynamoDB eventually consistent read.
createEndpointConfigRequest
- Future<CreateEndpointConfigResult> createEndpointConfigAsync(CreateEndpointConfigRequest createEndpointConfigRequest, AsyncHandler<CreateEndpointConfigRequest,CreateEndpointConfigResult> asyncHandler)
Creates an endpoint configuration that Amazon 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 Amazon SageMaker to provision. Then you call the CreateEndpoint API.
Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.
In the request, you define a ProductionVariant
, for each model that you want to deploy. Each
ProductionVariant
parameter also describes the resources that you want Amazon 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. Amazon SageMaker distributes two-thirds of the traffic to Model
A, and one-third to model B.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
exists. When you read data from a DynamoDB table supporting
Eventually Consistent Reads
, the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
this causes a validation error. If you repeat your read request after a short time, the response should return
the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
DynamoDB eventually consistent read.
createEndpointConfigRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateExperimentResult> createExperimentAsync(CreateExperimentRequest createExperimentRequest)
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 Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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.
createExperimentRequest
- Future<CreateExperimentResult> createExperimentAsync(CreateExperimentRequest createExperimentRequest, AsyncHandler<CreateExperimentRequest,CreateExperimentResult> asyncHandler)
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 Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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.
createExperimentRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateFeatureGroupResult> createFeatureGroupAsync(CreateFeatureGroupRequest createFeatureGroupRequest)
Create a new FeatureGroup
. A FeatureGroup
is a group of Features
defined
in the FeatureStore
to describe a Record
.
The FeatureGroup
defines the schema and features contained in the FeatureGroup. A
FeatureGroup
definition is composed of a list of Features
, a
RecordIdentifierFeatureName
, an EventTimeFeatureName
and configurations for its
OnlineStore
and OfflineStore
. Check AWS service quotas to see the
FeatureGroup
s quota for your AWS account.
You must include at least one of OnlineStoreConfig
and OfflineStoreConfig
to create a
FeatureGroup
.
createFeatureGroupRequest
- Future<CreateFeatureGroupResult> createFeatureGroupAsync(CreateFeatureGroupRequest createFeatureGroupRequest, AsyncHandler<CreateFeatureGroupRequest,CreateFeatureGroupResult> asyncHandler)
Create a new FeatureGroup
. A FeatureGroup
is a group of Features
defined
in the FeatureStore
to describe a Record
.
The FeatureGroup
defines the schema and features contained in the FeatureGroup. A
FeatureGroup
definition is composed of a list of Features
, a
RecordIdentifierFeatureName
, an EventTimeFeatureName
and configurations for its
OnlineStore
and OfflineStore
. Check AWS service quotas to see the
FeatureGroup
s quota for your AWS account.
You must include at least one of OnlineStoreConfig
and OfflineStoreConfig
to create a
FeatureGroup
.
createFeatureGroupRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateFlowDefinitionResult> createFlowDefinitionAsync(CreateFlowDefinitionRequest createFlowDefinitionRequest)
Creates a flow definition.
createFlowDefinitionRequest
- Future<CreateFlowDefinitionResult> createFlowDefinitionAsync(CreateFlowDefinitionRequest createFlowDefinitionRequest, AsyncHandler<CreateFlowDefinitionRequest,CreateFlowDefinitionResult> asyncHandler)
Creates a flow definition.
createFlowDefinitionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateHumanTaskUiResult> createHumanTaskUiAsync(CreateHumanTaskUiRequest createHumanTaskUiRequest)
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.
createHumanTaskUiRequest
- Future<CreateHumanTaskUiResult> createHumanTaskUiAsync(CreateHumanTaskUiRequest createHumanTaskUiRequest, AsyncHandler<CreateHumanTaskUiRequest,CreateHumanTaskUiResult> asyncHandler)
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.
createHumanTaskUiRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateHyperParameterTuningJobResult> createHyperParameterTuningJobAsync(CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest)
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.
createHyperParameterTuningJobRequest
- Future<CreateHyperParameterTuningJobResult> createHyperParameterTuningJobAsync(CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest, AsyncHandler<CreateHyperParameterTuningJobRequest,CreateHyperParameterTuningJobResult> asyncHandler)
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.
createHyperParameterTuningJobRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateImageResult> createImageAsync(CreateImageRequest createImageRequest)
Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image.
createImageRequest
- Future<CreateImageResult> createImageAsync(CreateImageRequest createImageRequest, AsyncHandler<CreateImageRequest,CreateImageResult> asyncHandler)
Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image.
createImageRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateImageVersionResult> createImageVersionAsync(CreateImageVersionRequest createImageVersionRequest)
Creates a version of the SageMaker image specified by ImageName
. The version represents the Amazon
Container Registry (ECR) container image specified by BaseImage
.
createImageVersionRequest
- Future<CreateImageVersionResult> createImageVersionAsync(CreateImageVersionRequest createImageVersionRequest, AsyncHandler<CreateImageVersionRequest,CreateImageVersionResult> asyncHandler)
Creates a version of the SageMaker image specified by ImageName
. The version represents the Amazon
Container Registry (ECR) container image specified by BaseImage
.
createImageVersionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateLabelingJobResult> createLabelingJobAsync(CreateLabelingJobRequest createLabelingJobRequest)
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 AWS 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.
createLabelingJobRequest
- Future<CreateLabelingJobResult> createLabelingJobAsync(CreateLabelingJobRequest createLabelingJobRequest, AsyncHandler<CreateLabelingJobRequest,CreateLabelingJobResult> asyncHandler)
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 AWS 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.
createLabelingJobRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateModelResult> createModelAsync(CreateModelRequest createModelRequest)
Creates a model in Amazon 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 Amazon SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the CreateEndpointConfig
API, and then
create an endpoint with the CreateEndpoint
API. Amazon 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 Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
To run a batch transform using your model, you start a job with the CreateTransformJob
API. Amazon
SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the CreateModel
request, you must define a container with the PrimaryContainer
parameter.
In the request, you also provide an IAM role that Amazon 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 AWS resources, you grant necessary permissions via this role.
createModelRequest
- Future<CreateModelResult> createModelAsync(CreateModelRequest createModelRequest, AsyncHandler<CreateModelRequest,CreateModelResult> asyncHandler)
Creates a model in Amazon 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 Amazon SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the CreateEndpointConfig
API, and then
create an endpoint with the CreateEndpoint
API. Amazon 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 Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
To run a batch transform using your model, you start a job with the CreateTransformJob
API. Amazon
SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the CreateModel
request, you must define a container with the PrimaryContainer
parameter.
In the request, you also provide an IAM role that Amazon 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 AWS resources, you grant necessary permissions via this role.
createModelRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateModelBiasJobDefinitionResult> createModelBiasJobDefinitionAsync(CreateModelBiasJobDefinitionRequest createModelBiasJobDefinitionRequest)
Creates the definition for a model bias job.
createModelBiasJobDefinitionRequest
- Future<CreateModelBiasJobDefinitionResult> createModelBiasJobDefinitionAsync(CreateModelBiasJobDefinitionRequest createModelBiasJobDefinitionRequest, AsyncHandler<CreateModelBiasJobDefinitionRequest,CreateModelBiasJobDefinitionResult> asyncHandler)
Creates the definition for a model bias job.
createModelBiasJobDefinitionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateModelExplainabilityJobDefinitionResult> createModelExplainabilityJobDefinitionAsync(CreateModelExplainabilityJobDefinitionRequest createModelExplainabilityJobDefinitionRequest)
Creates the definition for a model explainability job.
createModelExplainabilityJobDefinitionRequest
- Future<CreateModelExplainabilityJobDefinitionResult> createModelExplainabilityJobDefinitionAsync(CreateModelExplainabilityJobDefinitionRequest createModelExplainabilityJobDefinitionRequest, AsyncHandler<CreateModelExplainabilityJobDefinitionRequest,CreateModelExplainabilityJobDefinitionResult> asyncHandler)
Creates the definition for a model explainability job.
createModelExplainabilityJobDefinitionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateModelPackageResult> createModelPackageAsync(CreateModelPackageRequest createModelPackageRequest)
Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon 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 AWS Marketplace, provide a value for
SourceAlgorithmSpecification
.
There are two types of model packages:
Versioned - a model that is part of a model group in the model registry.
Unversioned - a model package that is not part of a model group.
createModelPackageRequest
- Future<CreateModelPackageResult> createModelPackageAsync(CreateModelPackageRequest createModelPackageRequest, AsyncHandler<CreateModelPackageRequest,CreateModelPackageResult> asyncHandler)
Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon 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 AWS Marketplace, provide a value for
SourceAlgorithmSpecification
.
There are two types of model packages:
Versioned - a model that is part of a model group in the model registry.
Unversioned - a model package that is not part of a model group.
createModelPackageRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateModelPackageGroupResult> createModelPackageGroupAsync(CreateModelPackageGroupRequest createModelPackageGroupRequest)
Creates a model group. A model group contains a group of model versions.
createModelPackageGroupRequest
- Future<CreateModelPackageGroupResult> createModelPackageGroupAsync(CreateModelPackageGroupRequest createModelPackageGroupRequest, AsyncHandler<CreateModelPackageGroupRequest,CreateModelPackageGroupResult> asyncHandler)
Creates a model group. A model group contains a group of model versions.
createModelPackageGroupRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateModelQualityJobDefinitionResult> createModelQualityJobDefinitionAsync(CreateModelQualityJobDefinitionRequest createModelQualityJobDefinitionRequest)
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
createModelQualityJobDefinitionRequest
- Future<CreateModelQualityJobDefinitionResult> createModelQualityJobDefinitionAsync(CreateModelQualityJobDefinitionRequest createModelQualityJobDefinitionRequest, AsyncHandler<CreateModelQualityJobDefinitionRequest,CreateModelQualityJobDefinitionResult> asyncHandler)
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
createModelQualityJobDefinitionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateMonitoringScheduleResult> createMonitoringScheduleAsync(CreateMonitoringScheduleRequest createMonitoringScheduleRequest)
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
createMonitoringScheduleRequest
- Future<CreateMonitoringScheduleResult> createMonitoringScheduleAsync(CreateMonitoringScheduleRequest createMonitoringScheduleRequest, AsyncHandler<CreateMonitoringScheduleRequest,CreateMonitoringScheduleResult> asyncHandler)
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
createMonitoringScheduleRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateNotebookInstanceResult> createNotebookInstanceAsync(CreateNotebookInstanceRequest createNotebookInstanceRequest)
Creates an Amazon 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.
Amazon 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.
Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, Amazon SageMaker does the following:
Creates a network interface in the Amazon SageMaker VPC.
(Option) If you specified SubnetId
, Amazon 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, Amazon
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 Amazon SageMaker VPC. If you specified
SubnetId
of your VPC, Amazon 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, Amazon SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After Amazon 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 Amazon SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
createNotebookInstanceRequest
- Future<CreateNotebookInstanceResult> createNotebookInstanceAsync(CreateNotebookInstanceRequest createNotebookInstanceRequest, AsyncHandler<CreateNotebookInstanceRequest,CreateNotebookInstanceResult> asyncHandler)
Creates an Amazon 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.
Amazon 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.
Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, Amazon SageMaker does the following:
Creates a network interface in the Amazon SageMaker VPC.
(Option) If you specified SubnetId
, Amazon 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, Amazon
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 Amazon SageMaker VPC. If you specified
SubnetId
of your VPC, Amazon 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, Amazon SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After Amazon 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 Amazon SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
createNotebookInstanceRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateNotebookInstanceLifecycleConfigResult> createNotebookInstanceLifecycleConfigAsync(CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest)
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.
createNotebookInstanceLifecycleConfigRequest
- Future<CreateNotebookInstanceLifecycleConfigResult> createNotebookInstanceLifecycleConfigAsync(CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest, AsyncHandler<CreateNotebookInstanceLifecycleConfigRequest,CreateNotebookInstanceLifecycleConfigResult> asyncHandler)
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.
createNotebookInstanceLifecycleConfigRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreatePipelineResult> createPipelineAsync(CreatePipelineRequest createPipelineRequest)
Creates a pipeline using a JSON pipeline definition.
createPipelineRequest
- Future<CreatePipelineResult> createPipelineAsync(CreatePipelineRequest createPipelineRequest, AsyncHandler<CreatePipelineRequest,CreatePipelineResult> asyncHandler)
Creates a pipeline using a JSON pipeline definition.
createPipelineRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreatePresignedDomainUrlResult> createPresignedDomainUrlAsync(CreatePresignedDomainUrlRequest createPresignedDomainUrlRequest)
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 URL that you get from a call to CreatePresignedDomainUrl
has a default timeout of 5 minutes. You
can configure this value using ExpiresInSeconds
. If you try to use the URL after the timeout limit
expires, you are directed to the AWS console sign-in page.
createPresignedDomainUrlRequest
- Future<CreatePresignedDomainUrlResult> createPresignedDomainUrlAsync(CreatePresignedDomainUrlRequest createPresignedDomainUrlRequest, AsyncHandler<CreatePresignedDomainUrlRequest,CreatePresignedDomainUrlResult> asyncHandler)
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 URL that you get from a call to CreatePresignedDomainUrl
has a default timeout of 5 minutes. You
can configure this value using ExpiresInSeconds
. If you try to use the URL after the timeout limit
expires, you are directed to the AWS console sign-in page.
createPresignedDomainUrlRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreatePresignedNotebookInstanceUrlResult> createPresignedNotebookInstanceUrlAsync(CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest)
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker
console, when you choose Open
next to a notebook instance, Amazon SageMaker opens a new tab showing
the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the
page.
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify.
Use the NotIpAddress
condition operator and the aws:SourceIP
condition context key to
specify the list of IP addresses that you want to have access to the notebook instance. For more information, see
Limit Access to a Notebook Instance by IP Address.
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
createPresignedNotebookInstanceUrlRequest
- Future<CreatePresignedNotebookInstanceUrlResult> createPresignedNotebookInstanceUrlAsync(CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest, AsyncHandler<CreatePresignedNotebookInstanceUrlRequest,CreatePresignedNotebookInstanceUrlResult> asyncHandler)
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker
console, when you choose Open
next to a notebook instance, Amazon SageMaker opens a new tab showing
the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the
page.
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify.
Use the NotIpAddress
condition operator and the aws:SourceIP
condition context key to
specify the list of IP addresses that you want to have access to the notebook instance. For more information, see
Limit Access to a Notebook Instance by IP Address.
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
createPresignedNotebookInstanceUrlRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateProcessingJobResult> createProcessingJobAsync(CreateProcessingJobRequest createProcessingJobRequest)
Creates a processing job.
createProcessingJobRequest
- Future<CreateProcessingJobResult> createProcessingJobAsync(CreateProcessingJobRequest createProcessingJobRequest, AsyncHandler<CreateProcessingJobRequest,CreateProcessingJobResult> asyncHandler)
Creates a processing job.
createProcessingJobRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateProjectResult> createProjectAsync(CreateProjectRequest createProjectRequest)
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.
createProjectRequest
- Future<CreateProjectResult> createProjectAsync(CreateProjectRequest createProjectRequest, AsyncHandler<CreateProjectRequest,CreateProjectResult> asyncHandler)
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.
createProjectRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateTrainingJobResult> createTrainingJobAsync(CreateTrainingJobRequest createTrainingJobRequest)
Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location 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 in a machine learning service other than Amazon 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 Amazon 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 Amazon 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 Amazon SageMaker assumes to perform tasks on your
behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can
successfully complete model training.
StoppingCondition
- To help cap training costs, use MaxRuntimeInSeconds
to set a time
limit for training. Use MaxWaitTimeInSeconds
to specify how long you are willing to wait for a
managed spot training job to complete.
Environment
- The environment variables to set in the Docker container.
For more information about Amazon SageMaker, see How It Works.
createTrainingJobRequest
- Future<CreateTrainingJobResult> createTrainingJobAsync(CreateTrainingJobRequest createTrainingJobRequest, AsyncHandler<CreateTrainingJobRequest,CreateTrainingJobResult> asyncHandler)
Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location 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 in a machine learning service other than Amazon 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 Amazon 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 Amazon 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 Amazon SageMaker assumes to perform tasks on your
behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can
successfully complete model training.
StoppingCondition
- To help cap training costs, use MaxRuntimeInSeconds
to set a time
limit for training. Use MaxWaitTimeInSeconds
to specify how long you are willing to wait for a
managed spot training job to complete.
Environment
- The environment variables to set in the Docker container.
For more information about Amazon SageMaker, see How It Works.
createTrainingJobRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateTransformJobResult> createTransformJobAsync(CreateTransformJobRequest createTransformJobRequest)
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 AWS Region in an
AWS account.
ModelName
- Identifies the model to use. ModelName
must be the name of an existing
Amazon SageMaker model in the same AWS Region and AWS 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.
createTransformJobRequest
- Future<CreateTransformJobResult> createTransformJobAsync(CreateTransformJobRequest createTransformJobRequest, AsyncHandler<CreateTransformJobRequest,CreateTransformJobResult> asyncHandler)
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 AWS Region in an
AWS account.
ModelName
- Identifies the model to use. ModelName
must be the name of an existing
Amazon SageMaker model in the same AWS Region and AWS 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.
createTransformJobRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateTrialResult> createTrialAsync(CreateTrialRequest createTrialRequest)
Creates an Amazon SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single Amazon SageMaker experiment.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
createTrialRequest
- Future<CreateTrialResult> createTrialAsync(CreateTrialRequest createTrialRequest, AsyncHandler<CreateTrialRequest,CreateTrialResult> asyncHandler)
Creates an Amazon SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single Amazon SageMaker experiment.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
createTrialRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateTrialComponentResult> createTrialComponentAsync(CreateTrialComponentRequest createTrialComponentRequest)
Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
CreateTrialComponent
can only be invoked from within an Amazon SageMaker managed environment. This
includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call
to CreateTrialComponent
from outside one of these environments results in an error.
createTrialComponentRequest
- Future<CreateTrialComponentResult> createTrialComponentAsync(CreateTrialComponentRequest createTrialComponentRequest, AsyncHandler<CreateTrialComponentRequest,CreateTrialComponentResult> asyncHandler)
Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
CreateTrialComponent
can only be invoked from within an Amazon SageMaker managed environment. This
includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call
to CreateTrialComponent
from outside one of these environments results in an error.
createTrialComponentRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateUserProfileResult> createUserProfileAsync(CreateUserProfileRequest createUserProfileRequest)
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
createUserProfileRequest
- Future<CreateUserProfileResult> createUserProfileAsync(CreateUserProfileRequest createUserProfileRequest, AsyncHandler<CreateUserProfileRequest,CreateUserProfileResult> asyncHandler)
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
createUserProfileRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateWorkforceResult> createWorkforceAsync(CreateWorkforceRequest createWorkforceRequest)
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the AWS Region that you specify. You can only create one workforce in each AWS Region per AWS account.
If you want to create a new workforce in an AWS Region where a workforce already exists, use the API operation to
delete the existing workforce and then use CreateWorkforce
to create a new workforce.
To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in
CognitoConfig
. You can also create an Amazon Cognito workforce using the Amazon SageMaker console.
For more information, see Create a Private
Workforce (Amazon Cognito).
To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in
OidcConfig
. Your OIDC IdP must support groups because groups are used by Ground Truth and
Amazon A2I to create work teams. For more information, see Create a Private
Workforce (OIDC IdP).
createWorkforceRequest
- Future<CreateWorkforceResult> createWorkforceAsync(CreateWorkforceRequest createWorkforceRequest, AsyncHandler<CreateWorkforceRequest,CreateWorkforceResult> asyncHandler)
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the AWS Region that you specify. You can only create one workforce in each AWS Region per AWS account.
If you want to create a new workforce in an AWS Region where a workforce already exists, use the API operation to
delete the existing workforce and then use CreateWorkforce
to create a new workforce.
To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in
CognitoConfig
. You can also create an Amazon Cognito workforce using the Amazon SageMaker console.
For more information, see Create a Private
Workforce (Amazon Cognito).
To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in
OidcConfig
. Your OIDC IdP must support groups because groups are used by Ground Truth and
Amazon A2I to create work teams. For more information, see Create a Private
Workforce (OIDC IdP).
createWorkforceRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateWorkteamResult> createWorkteamAsync(CreateWorkteamRequest createWorkteamRequest)
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
You cannot create more than 25 work teams in an account and region.
createWorkteamRequest
- Future<CreateWorkteamResult> createWorkteamAsync(CreateWorkteamRequest createWorkteamRequest, AsyncHandler<CreateWorkteamRequest,CreateWorkteamResult> asyncHandler)
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
You cannot create more than 25 work teams in an account and region.
createWorkteamRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteActionResult> deleteActionAsync(DeleteActionRequest deleteActionRequest)
Deletes an action.
deleteActionRequest
- Future<DeleteActionResult> deleteActionAsync(DeleteActionRequest deleteActionRequest, AsyncHandler<DeleteActionRequest,DeleteActionResult> asyncHandler)
Deletes an action.
deleteActionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteAlgorithmResult> deleteAlgorithmAsync(DeleteAlgorithmRequest deleteAlgorithmRequest)
Removes the specified algorithm from your account.
deleteAlgorithmRequest
- Future<DeleteAlgorithmResult> deleteAlgorithmAsync(DeleteAlgorithmRequest deleteAlgorithmRequest, AsyncHandler<DeleteAlgorithmRequest,DeleteAlgorithmResult> asyncHandler)
Removes the specified algorithm from your account.
deleteAlgorithmRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteAppResult> deleteAppAsync(DeleteAppRequest deleteAppRequest)
Used to stop and delete an app.
deleteAppRequest
- Future<DeleteAppResult> deleteAppAsync(DeleteAppRequest deleteAppRequest, AsyncHandler<DeleteAppRequest,DeleteAppResult> asyncHandler)
Used to stop and delete an app.
deleteAppRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteAppImageConfigResult> deleteAppImageConfigAsync(DeleteAppImageConfigRequest deleteAppImageConfigRequest)
Deletes an AppImageConfig.
deleteAppImageConfigRequest
- Future<DeleteAppImageConfigResult> deleteAppImageConfigAsync(DeleteAppImageConfigRequest deleteAppImageConfigRequest, AsyncHandler<DeleteAppImageConfigRequest,DeleteAppImageConfigResult> asyncHandler)
Deletes an AppImageConfig.
deleteAppImageConfigRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteArtifactResult> deleteArtifactAsync(DeleteArtifactRequest deleteArtifactRequest)
Deletes an artifact. Either ArtifactArn
or Source
must be specified.
deleteArtifactRequest
- Future<DeleteArtifactResult> deleteArtifactAsync(DeleteArtifactRequest deleteArtifactRequest, AsyncHandler<DeleteArtifactRequest,DeleteArtifactResult> asyncHandler)
Deletes an artifact. Either ArtifactArn
or Source
must be specified.
deleteArtifactRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteAssociationResult> deleteAssociationAsync(DeleteAssociationRequest deleteAssociationRequest)
Deletes an association.
deleteAssociationRequest
- Future<DeleteAssociationResult> deleteAssociationAsync(DeleteAssociationRequest deleteAssociationRequest, AsyncHandler<DeleteAssociationRequest,DeleteAssociationResult> asyncHandler)
Deletes an association.
deleteAssociationRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteCodeRepositoryResult> deleteCodeRepositoryAsync(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest)
Deletes the specified Git repository from your account.
deleteCodeRepositoryRequest
- Future<DeleteCodeRepositoryResult> deleteCodeRepositoryAsync(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest, AsyncHandler<DeleteCodeRepositoryRequest,DeleteCodeRepositoryResult> asyncHandler)
Deletes the specified Git repository from your account.
deleteCodeRepositoryRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteContextResult> deleteContextAsync(DeleteContextRequest deleteContextRequest)
Deletes an context.
deleteContextRequest
- Future<DeleteContextResult> deleteContextAsync(DeleteContextRequest deleteContextRequest, AsyncHandler<DeleteContextRequest,DeleteContextResult> asyncHandler)
Deletes an context.
deleteContextRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteDataQualityJobDefinitionResult> deleteDataQualityJobDefinitionAsync(DeleteDataQualityJobDefinitionRequest deleteDataQualityJobDefinitionRequest)
Deletes a data quality monitoring job definition.
deleteDataQualityJobDefinitionRequest
- Future<DeleteDataQualityJobDefinitionResult> deleteDataQualityJobDefinitionAsync(DeleteDataQualityJobDefinitionRequest deleteDataQualityJobDefinitionRequest, AsyncHandler<DeleteDataQualityJobDefinitionRequest,DeleteDataQualityJobDefinitionResult> asyncHandler)
Deletes a data quality monitoring job definition.
deleteDataQualityJobDefinitionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteDeviceFleetResult> deleteDeviceFleetAsync(DeleteDeviceFleetRequest deleteDeviceFleetRequest)
Deletes a fleet.
deleteDeviceFleetRequest
- Future<DeleteDeviceFleetResult> deleteDeviceFleetAsync(DeleteDeviceFleetRequest deleteDeviceFleetRequest, AsyncHandler<DeleteDeviceFleetRequest,DeleteDeviceFleetResult> asyncHandler)
Deletes a fleet.
deleteDeviceFleetRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteDomainResult> deleteDomainAsync(DeleteDomainRequest deleteDomainRequest)
Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
deleteDomainRequest
- Future<DeleteDomainResult> deleteDomainAsync(DeleteDomainRequest deleteDomainRequest, AsyncHandler<DeleteDomainRequest,DeleteDomainResult> asyncHandler)
Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
deleteDomainRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteEndpointResult> deleteEndpointAsync(DeleteEndpointRequest deleteEndpointRequest)
Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.
Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
deleteEndpointRequest
- Future<DeleteEndpointResult> deleteEndpointAsync(DeleteEndpointRequest deleteEndpointRequest, AsyncHandler<DeleteEndpointRequest,DeleteEndpointResult> asyncHandler)
Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.
Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
deleteEndpointRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteEndpointConfigResult> deleteEndpointConfigAsync(DeleteEndpointConfigRequest deleteEndpointConfigRequest)
Deletes an endpoint configuration. The DeleteEndpointConfig
API deletes only the specified
configuration. It does not delete endpoints created using the configuration.
You must not delete an EndpointConfig
in use by an endpoint that is live or while the
UpdateEndpoint
or CreateEndpoint
operations are being performed on the endpoint. If you
delete the EndpointConfig
of an endpoint that is active or being created or updated you may lose
visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring
charges.
deleteEndpointConfigRequest
- Future<DeleteEndpointConfigResult> deleteEndpointConfigAsync(DeleteEndpointConfigRequest deleteEndpointConfigRequest, AsyncHandler<DeleteEndpointConfigRequest,DeleteEndpointConfigResult> asyncHandler)
Deletes an endpoint configuration. The DeleteEndpointConfig
API deletes only the specified
configuration. It does not delete endpoints created using the configuration.
You must not delete an EndpointConfig
in use by an endpoint that is live or while the
UpdateEndpoint
or CreateEndpoint
operations are being performed on the endpoint. If you
delete the EndpointConfig
of an endpoint that is active or being created or updated you may lose
visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring
charges.
deleteEndpointConfigRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteExperimentResult> deleteExperimentAsync(DeleteExperimentRequest deleteExperimentRequest)
Deletes an Amazon SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
deleteExperimentRequest
- Future<DeleteExperimentResult> deleteExperimentAsync(DeleteExperimentRequest deleteExperimentRequest, AsyncHandler<DeleteExperimentRequest,DeleteExperimentResult> asyncHandler)
Deletes an Amazon SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
deleteExperimentRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteFeatureGroupResult> deleteFeatureGroupAsync(DeleteFeatureGroupRequest deleteFeatureGroupRequest)
Delete the FeatureGroup
and any data that was written to the OnlineStore
of the
FeatureGroup
. Data cannot be accessed from the OnlineStore
immediately after
DeleteFeatureGroup
is called.
Data written into the OfflineStore
will not be deleted. The AWS Glue database and tables that are
automatically created for your OfflineStore
are not deleted.
deleteFeatureGroupRequest
- Future<DeleteFeatureGroupResult> deleteFeatureGroupAsync(DeleteFeatureGroupRequest deleteFeatureGroupRequest, AsyncHandler<DeleteFeatureGroupRequest,DeleteFeatureGroupResult> asyncHandler)
Delete the FeatureGroup
and any data that was written to the OnlineStore
of the
FeatureGroup
. Data cannot be accessed from the OnlineStore
immediately after
DeleteFeatureGroup
is called.
Data written into the OfflineStore
will not be deleted. The AWS Glue database and tables that are
automatically created for your OfflineStore
are not deleted.
deleteFeatureGroupRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteFlowDefinitionResult> deleteFlowDefinitionAsync(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest)
Deletes the specified flow definition.
deleteFlowDefinitionRequest
- Future<DeleteFlowDefinitionResult> deleteFlowDefinitionAsync(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest, AsyncHandler<DeleteFlowDefinitionRequest,DeleteFlowDefinitionResult> asyncHandler)
Deletes the specified flow definition.
deleteFlowDefinitionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteHumanTaskUiResult> deleteHumanTaskUiAsync(DeleteHumanTaskUiRequest deleteHumanTaskUiRequest)
Use this operation to delete a human task user interface (worker task template).
To see a list of human task user interfaces (work task templates) in your account, use . When you delete a worker
task template, it no longer appears when you call ListHumanTaskUis
.
deleteHumanTaskUiRequest
- Future<DeleteHumanTaskUiResult> deleteHumanTaskUiAsync(DeleteHumanTaskUiRequest deleteHumanTaskUiRequest, AsyncHandler<DeleteHumanTaskUiRequest,DeleteHumanTaskUiResult> asyncHandler)
Use this operation to delete a human task user interface (worker task template).
To see a list of human task user interfaces (work task templates) in your account, use . When you delete a worker
task template, it no longer appears when you call ListHumanTaskUis
.
deleteHumanTaskUiRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteImageResult> deleteImageAsync(DeleteImageRequest deleteImageRequest)
Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
deleteImageRequest
- Future<DeleteImageResult> deleteImageAsync(DeleteImageRequest deleteImageRequest, AsyncHandler<DeleteImageRequest,DeleteImageResult> asyncHandler)
Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
deleteImageRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteImageVersionResult> deleteImageVersionAsync(DeleteImageVersionRequest deleteImageVersionRequest)
Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
deleteImageVersionRequest
- Future<DeleteImageVersionResult> deleteImageVersionAsync(DeleteImageVersionRequest deleteImageVersionRequest, AsyncHandler<DeleteImageVersionRequest,DeleteImageVersionResult> asyncHandler)
Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
deleteImageVersionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteModelResult> deleteModelAsync(DeleteModelRequest deleteModelRequest)
Deletes a model. The DeleteModel
API deletes only the model entry that was created in Amazon
SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the
IAM role that you specified when creating the model.
deleteModelRequest
- Future<DeleteModelResult> deleteModelAsync(DeleteModelRequest deleteModelRequest, AsyncHandler<DeleteModelRequest,DeleteModelResult> asyncHandler)
Deletes a model. The DeleteModel
API deletes only the model entry that was created in Amazon
SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the
IAM role that you specified when creating the model.
deleteModelRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteModelBiasJobDefinitionResult> deleteModelBiasJobDefinitionAsync(DeleteModelBiasJobDefinitionRequest deleteModelBiasJobDefinitionRequest)
Deletes an Amazon SageMaker model bias job definition.
deleteModelBiasJobDefinitionRequest
- Future<DeleteModelBiasJobDefinitionResult> deleteModelBiasJobDefinitionAsync(DeleteModelBiasJobDefinitionRequest deleteModelBiasJobDefinitionRequest, AsyncHandler<DeleteModelBiasJobDefinitionRequest,DeleteModelBiasJobDefinitionResult> asyncHandler)
Deletes an Amazon SageMaker model bias job definition.
deleteModelBiasJobDefinitionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteModelExplainabilityJobDefinitionResult> deleteModelExplainabilityJobDefinitionAsync(DeleteModelExplainabilityJobDefinitionRequest deleteModelExplainabilityJobDefinitionRequest)
Deletes an Amazon SageMaker model explainability job definition.
deleteModelExplainabilityJobDefinitionRequest
- Future<DeleteModelExplainabilityJobDefinitionResult> deleteModelExplainabilityJobDefinitionAsync(DeleteModelExplainabilityJobDefinitionRequest deleteModelExplainabilityJobDefinitionRequest, AsyncHandler<DeleteModelExplainabilityJobDefinitionRequest,DeleteModelExplainabilityJobDefinitionResult> asyncHandler)
Deletes an Amazon SageMaker model explainability job definition.
deleteModelExplainabilityJobDefinitionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteModelPackageResult> deleteModelPackageAsync(DeleteModelPackageRequest deleteModelPackageRequest)
Deletes a model package.
A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
deleteModelPackageRequest
- Future<DeleteModelPackageResult> deleteModelPackageAsync(DeleteModelPackageRequest deleteModelPackageRequest, AsyncHandler<DeleteModelPackageRequest,DeleteModelPackageResult> asyncHandler)
Deletes a model package.
A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
deleteModelPackageRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteModelPackageGroupResult> deleteModelPackageGroupAsync(DeleteModelPackageGroupRequest deleteModelPackageGroupRequest)
Deletes the specified model group.
deleteModelPackageGroupRequest
- Future<DeleteModelPackageGroupResult> deleteModelPackageGroupAsync(DeleteModelPackageGroupRequest deleteModelPackageGroupRequest, AsyncHandler<DeleteModelPackageGroupRequest,DeleteModelPackageGroupResult> asyncHandler)
Deletes the specified model group.
deleteModelPackageGroupRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteModelPackageGroupPolicyResult> deleteModelPackageGroupPolicyAsync(DeleteModelPackageGroupPolicyRequest deleteModelPackageGroupPolicyRequest)
Deletes a model group resource policy.
deleteModelPackageGroupPolicyRequest
- Future<DeleteModelPackageGroupPolicyResult> deleteModelPackageGroupPolicyAsync(DeleteModelPackageGroupPolicyRequest deleteModelPackageGroupPolicyRequest, AsyncHandler<DeleteModelPackageGroupPolicyRequest,DeleteModelPackageGroupPolicyResult> asyncHandler)
Deletes a model group resource policy.
deleteModelPackageGroupPolicyRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteModelQualityJobDefinitionResult> deleteModelQualityJobDefinitionAsync(DeleteModelQualityJobDefinitionRequest deleteModelQualityJobDefinitionRequest)
Deletes the secified model quality monitoring job definition.
deleteModelQualityJobDefinitionRequest
- Future<DeleteModelQualityJobDefinitionResult> deleteModelQualityJobDefinitionAsync(DeleteModelQualityJobDefinitionRequest deleteModelQualityJobDefinitionRequest, AsyncHandler<DeleteModelQualityJobDefinitionRequest,DeleteModelQualityJobDefinitionResult> asyncHandler)
Deletes the secified model quality monitoring job definition.
deleteModelQualityJobDefinitionRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteMonitoringScheduleResult> deleteMonitoringScheduleAsync(DeleteMonitoringScheduleRequest deleteMonitoringScheduleRequest)
Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
deleteMonitoringScheduleRequest
- Future<DeleteMonitoringScheduleResult> deleteMonitoringScheduleAsync(DeleteMonitoringScheduleRequest deleteMonitoringScheduleRequest, AsyncHandler<DeleteMonitoringScheduleRequest,DeleteMonitoringScheduleResult> asyncHandler)
Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
deleteMonitoringScheduleRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteNotebookInstanceResult> deleteNotebookInstanceAsync(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest)
Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the
StopNotebookInstance
API.
When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
deleteNotebookInstanceRequest
- Future<DeleteNotebookInstanceResult> deleteNotebookInstanceAsync(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest, AsyncHandler<DeleteNotebookInstanceRequest,DeleteNotebookInstanceResult> asyncHandler)
Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the
StopNotebookInstance
API.
When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
deleteNotebookInstanceRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteNotebookInstanceLifecycleConfigResult> deleteNotebookInstanceLifecycleConfigAsync(DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest)
Deletes a notebook instance lifecycle configuration.
deleteNotebookInstanceLifecycleConfigRequest
- Future<DeleteNotebookInstanceLifecycleConfigResult> deleteNotebookInstanceLifecycleConfigAsync(DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest, AsyncHandler<DeleteNotebookInstanceLifecycleConfigRequest,DeleteNotebookInstanceLifecycleConfigResult> asyncHandler)
Deletes a notebook instance lifecycle configuration.
deleteNotebookInstanceLifecycleConfigRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeletePipelineResult> deletePipelineAsync(DeletePipelineRequest deletePipelineRequest)
Deletes a pipeline if there are no in-progress executions.
deletePipelineRequest
- Future<DeletePipelineResult> deletePipelineAsync(DeletePipelineRequest deletePipelineRequest, AsyncHandler<DeletePipelineRequest,DeletePipelineResult> asyncHandler)
Deletes a pipeline if there are no in-progress executions.
deletePipelineRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteProjectResult> deleteProjectAsync(DeleteProjectRequest deleteProjectRequest)
Delete the specified project.
deleteProjectRequest
- Future<DeleteProjectResult> deleteProjectAsync(DeleteProjectRequest deleteProjectRequest, AsyncHandler<DeleteProjectRequest,DeleteProjectResult> asyncHandler)
Delete the specified project.
deleteProjectRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest deleteTagsRequest)
Deletes the specified tags from an Amazon SageMaker resource.
To list a resource's tags, use the ListTags
API.
When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.
deleteTagsRequest
- Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest deleteTagsRequest, AsyncHandler<DeleteTagsRequest,DeleteTagsResult> asyncHandler)
Deletes the specified tags from an Amazon SageMaker resource.
To list a resource's tags, use the ListTags
API.
When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.
deleteTagsRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteTrialResult> deleteTrialAsync(DeleteTrialRequest deleteTrialRequest)
Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
deleteTrialRequest
- Future<DeleteTrialResult> deleteTrialAsync(DeleteTrialRequest deleteTrialRequest, AsyncHandler<DeleteTrialRequest,DeleteTrialResult> asyncHandler)
Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
deleteTrialRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteTrialComponentResult> deleteTrialComponentAsync(DeleteTrialComponentRequest deleteTrialComponentRequest)
Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
deleteTrialComponentRequest
- Future<DeleteTrialComponentResult> deleteTrialComponentAsync(DeleteTrialComponentRequest deleteTrialComponentRequest, AsyncHandler<DeleteTrialComponentRequest,DeleteTrialComponentResult> asyncHandler)
Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
deleteTrialComponentRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteUserProfileResult> deleteUserProfileAsync(DeleteUserProfileRequest deleteUserProfileRequest)
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
deleteUserProfileRequest
- Future<DeleteUserProfileResult> deleteUserProfileAsync(DeleteUserProfileRequest deleteUserProfileRequest, AsyncHandler<DeleteUserProfileRequest,DeleteUserProfileResult> asyncHandler)
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
deleteUserProfileRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteWorkforceResult> deleteWorkforceAsync(DeleteWorkforceRequest deleteWorkforceRequest)
Use this operation to delete a workforce.
If you want to create a new workforce in an AWS Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.
If a private workforce contains one or more work teams, you must use the operation to delete all work teams
before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will
recieve a ResourceInUse
error.
deleteWorkforceRequest
- Future<DeleteWorkforceResult> deleteWorkforceAsync(DeleteWorkforceRequest deleteWorkforceRequest, AsyncHandler<DeleteWorkforceRequest,DeleteWorkforceResult> asyncHandler)
Use this operation to delete a workforce.
If you want to create a new workforce in an AWS Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.
If a private workforce contains one or more work teams, you must use the operation to delete all work teams
before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will
recieve a ResourceInUse
error.
deleteWorkforceRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteWorkteamResult> deleteWorkteamAsync(DeleteWorkteamRequest deleteWorkteamRequest)
Deletes an existing work team. This operation can't be undone.
deleteWorkteamRequest
- Future<DeleteWorkteamResult> deleteWorkteamAsync(DeleteWorkteamRequest deleteWorkteamRequest, AsyncHandler<DeleteWorkteamRequest,DeleteWorkteamResult> asyncHandler)
Deletes an existing work team. This operation can't be undone.
deleteWorkteamRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeregisterDevicesResult> deregisterDevicesAsync(DeregisterDevicesRequest deregisterDevicesRequest)
Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
deregisterDevicesRequest
- Future<DeregisterDevicesResult> deregisterDevicesAsync(DeregisterDevicesRequest deregisterDevicesRequest, AsyncHandler<DeregisterDevicesRequest,DeregisterDevicesResult> asyncHandler)
Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
deregisterDevicesRequest
- asyncHandler
- Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DescribeActionResult> describeActionAsync(DescribeActionRequest describeActionRequest)
Describes an action.
describeActionRequest
-