com.amazonaws.services.sagemaker.AmazonSageMakerAsync Maven / Gradle / Ivy
/*
* Copyright 2015-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.sagemaker;
import javax.annotation.Generated;
import com.amazonaws.services.sagemaker.model.*;
/**
* Interface for accessing SageMaker asynchronously. Each asynchronous method will return a Java Future object
* representing the asynchronous operation; overloads which accept an {@code 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
* {@link com.amazonaws.services.sagemaker.AbstractAmazonSageMakerAsync} instead.
*
*
*
* Provides APIs for creating and managing Amazon SageMaker resources.
*
*
* Other Resources:
*
*
* -
*
*
* -
*
*
*
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public interface AmazonSageMakerAsync extends AmazonSageMaker {
/**
*
* 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.
*
*
* @param addAssociationRequest
* @return A Java Future containing the result of the AddAssociation operation returned by the service.
* @sample AmazonSageMakerAsync.AddAssociation
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param addAssociationRequest
* @param 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.
* @return A Java Future containing the result of the AddAssociation operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.AddAssociation
* @see AWS API
* Documentation
*/
java.util.concurrent.Future addAssociationAsync(AddAssociationRequest addAssociationRequest,
com.amazonaws.handlers.AsyncHandler 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
*
*
*
* @param addTagsRequest
* @return A Java Future containing the result of the AddTags operation returned by the service.
* @sample AmazonSageMakerAsync.AddTags
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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
*
*
*
* @param addTagsRequest
* @param 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.
* @return A Java Future containing the result of the AddTags operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.AddTags
* @see AWS API
* Documentation
*/
java.util.concurrent.Future addTagsAsync(AddTagsRequest addTagsRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param associateTrialComponentRequest
* @return A Java Future containing the result of the AssociateTrialComponent operation returned by the service.
* @sample AmazonSageMakerAsync.AssociateTrialComponent
* @see AWS API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param associateTrialComponentRequest
* @param 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.
* @return A Java Future containing the result of the AssociateTrialComponent operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.AssociateTrialComponent
* @see AWS API Documentation
*/
java.util.concurrent.Future associateTrialComponentAsync(AssociateTrialComponentRequest associateTrialComponentRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createActionRequest
* @return A Java Future containing the result of the CreateAction operation returned by the service.
* @sample AmazonSageMakerAsync.CreateAction
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createActionRequest
* @param 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.
* @return A Java Future containing the result of the CreateAction operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateAction
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createActionAsync(CreateActionRequest createActionRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
*
*
* @param createAlgorithmRequest
* @return A Java Future containing the result of the CreateAlgorithm operation returned by the service.
* @sample AmazonSageMakerAsync.CreateAlgorithm
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createAlgorithmAsync(CreateAlgorithmRequest createAlgorithmRequest);
/**
*
* Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
*
*
* @param createAlgorithmRequest
* @param 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.
* @return A Java Future containing the result of the CreateAlgorithm operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateAlgorithm
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createAlgorithmAsync(CreateAlgorithmRequest createAlgorithmRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createAppRequest
* @return A Java Future containing the result of the CreateApp operation returned by the service.
* @sample AmazonSageMakerAsync.CreateApp
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createAppRequest
* @param 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.
* @return A Java Future containing the result of the CreateApp operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateApp
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createAppAsync(CreateAppRequest createAppRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createAppImageConfigRequest
* @return A Java Future containing the result of the CreateAppImageConfig operation returned by the service.
* @sample AmazonSageMakerAsync.CreateAppImageConfig
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createAppImageConfigRequest
* @param 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.
* @return A Java Future containing the result of the CreateAppImageConfig operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateAppImageConfig
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createAppImageConfigAsync(CreateAppImageConfigRequest createAppImageConfigRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createArtifactRequest
* @return A Java Future containing the result of the CreateArtifact operation returned by the service.
* @sample AmazonSageMakerAsync.CreateArtifact
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createArtifactRequest
* @param 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.
* @return A Java Future containing the result of the CreateArtifact operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateArtifact
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createArtifactAsync(CreateArtifactRequest createArtifactRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates an Autopilot job.
*
*
* Find the best performing model after you run an Autopilot job by calling . Deploy that model by following the
* steps described in Step 6.1:
* Deploy the Model to Amazon SageMaker Hosting Services.
*
*
* For information about how to use Autopilot, see Automate Model
* Development with Amazon SageMaker Autopilot.
*
*
* @param createAutoMLJobRequest
* @return A Java Future containing the result of the CreateAutoMLJob operation returned by the service.
* @sample AmazonSageMakerAsync.CreateAutoMLJob
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createAutoMLJobAsync(CreateAutoMLJobRequest createAutoMLJobRequest);
/**
*
* Creates an Autopilot job.
*
*
* Find the best performing model after you run an Autopilot job by calling . Deploy that model by following the
* steps described in Step 6.1:
* Deploy the Model to Amazon SageMaker Hosting Services.
*
*
* For information about how to use Autopilot, see Automate Model
* Development with Amazon SageMaker Autopilot.
*
*
* @param createAutoMLJobRequest
* @param 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.
* @return A Java Future containing the result of the CreateAutoMLJob operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateAutoMLJob
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createAutoMLJobAsync(CreateAutoMLJobRequest createAutoMLJobRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createCodeRepositoryRequest
* @return A Java Future containing the result of the CreateCodeRepository operation returned by the service.
* @sample AmazonSageMakerAsync.CreateCodeRepository
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createCodeRepositoryRequest
* @param 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.
* @return A Java Future containing the result of the CreateCodeRepository operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateCodeRepository
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createCodeRepositoryAsync(CreateCodeRepositoryRequest createCodeRepositoryRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createCompilationJobRequest
* @return A Java Future containing the result of the CreateCompilationJob operation returned by the service.
* @sample AmazonSageMakerAsync.CreateCompilationJob
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createCompilationJobRequest
* @param 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.
* @return A Java Future containing the result of the CreateCompilationJob operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateCompilationJob
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createCompilationJobAsync(CreateCompilationJobRequest createCompilationJobRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createContextRequest
* @return A Java Future containing the result of the CreateContext operation returned by the service.
* @sample AmazonSageMakerAsync.CreateContext
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createContextRequest
* @param 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.
* @return A Java Future containing the result of the CreateContext operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateContext
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createContextAsync(CreateContextRequest createContextRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
*
*
* @param createDataQualityJobDefinitionRequest
* @return A Java Future containing the result of the CreateDataQualityJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsync.CreateDataQualityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createDataQualityJobDefinitionRequest
* @param 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.
* @return A Java Future containing the result of the CreateDataQualityJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsyncHandler.CreateDataQualityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future createDataQualityJobDefinitionAsync(
CreateDataQualityJobDefinitionRequest createDataQualityJobDefinitionRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates a device fleet.
*
*
* @param createDeviceFleetRequest
* @return A Java Future containing the result of the CreateDeviceFleet operation returned by the service.
* @sample AmazonSageMakerAsync.CreateDeviceFleet
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createDeviceFleetAsync(CreateDeviceFleetRequest createDeviceFleetRequest);
/**
*
* Creates a device fleet.
*
*
* @param createDeviceFleetRequest
* @param 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.
* @return A Java Future containing the result of the CreateDeviceFleet operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateDeviceFleet
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createDeviceFleetAsync(CreateDeviceFleetRequest createDeviceFleetRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createDomainRequest
* @return A Java Future containing the result of the CreateDomain operation returned by the service.
* @sample AmazonSageMakerAsync.CreateDomain
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createDomainRequest
* @param 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.
* @return A Java Future containing the result of the CreateDomain operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateDomain
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createDomainAsync(CreateDomainRequest createDomainRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createEdgePackagingJobRequest
* @return A Java Future containing the result of the CreateEdgePackagingJob operation returned by the service.
* @sample AmazonSageMakerAsync.CreateEdgePackagingJob
* @see AWS API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createEdgePackagingJobRequest
* @param 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.
* @return A Java Future containing the result of the CreateEdgePackagingJob operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateEdgePackagingJob
* @see AWS API Documentation
*/
java.util.concurrent.Future createEdgePackagingJobAsync(CreateEdgePackagingJobRequest createEdgePackagingJobRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
*
*
*
* @param createEndpointRequest
* @return A Java Future containing the result of the CreateEndpoint operation returned by the service.
* @sample AmazonSageMakerAsync.CreateEndpoint
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
*
*
*
* @param createEndpointRequest
* @param 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.
* @return A Java Future containing the result of the CreateEndpoint operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateEndpoint
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createEndpointAsync(CreateEndpointRequest createEndpointRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
*
* @param createEndpointConfigRequest
* @return A Java Future containing the result of the CreateEndpointConfig operation returned by the service.
* @sample AmazonSageMakerAsync.CreateEndpointConfig
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
*
* @param createEndpointConfigRequest
* @param 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.
* @return A Java Future containing the result of the CreateEndpointConfig operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateEndpointConfig
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createEndpointConfigAsync(CreateEndpointConfigRequest createEndpointConfigRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createExperimentRequest
* @return A Java Future containing the result of the CreateExperiment operation returned by the service.
* @sample AmazonSageMakerAsync.CreateExperiment
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createExperimentRequest
* @param 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.
* @return A Java Future containing the result of the CreateExperiment operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateExperiment
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createExperimentAsync(CreateExperimentRequest createExperimentRequest,
com.amazonaws.handlers.AsyncHandler 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
* FeatureGroups quota for your AWS account.
*
*
*
* You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a
* FeatureGroup.
*
*
*
* @param createFeatureGroupRequest
* @return A Java Future containing the result of the CreateFeatureGroup operation returned by the service.
* @sample AmazonSageMakerAsync.CreateFeatureGroup
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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
* FeatureGroups quota for your AWS account.
*
*
*
* You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a
* FeatureGroup.
*
*
*
* @param createFeatureGroupRequest
* @param 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.
* @return A Java Future containing the result of the CreateFeatureGroup operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateFeatureGroup
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createFeatureGroupAsync(CreateFeatureGroupRequest createFeatureGroupRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates a flow definition.
*
*
* @param createFlowDefinitionRequest
* @return A Java Future containing the result of the CreateFlowDefinition operation returned by the service.
* @sample AmazonSageMakerAsync.CreateFlowDefinition
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createFlowDefinitionAsync(CreateFlowDefinitionRequest createFlowDefinitionRequest);
/**
*
* Creates a flow definition.
*
*
* @param createFlowDefinitionRequest
* @param 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.
* @return A Java Future containing the result of the CreateFlowDefinition operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateFlowDefinition
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createFlowDefinitionAsync(CreateFlowDefinitionRequest createFlowDefinitionRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createHumanTaskUiRequest
* @return A Java Future containing the result of the CreateHumanTaskUi operation returned by the service.
* @sample AmazonSageMakerAsync.CreateHumanTaskUi
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createHumanTaskUiRequest
* @param 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.
* @return A Java Future containing the result of the CreateHumanTaskUi operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateHumanTaskUi
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createHumanTaskUiAsync(CreateHumanTaskUiRequest createHumanTaskUiRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createHyperParameterTuningJobRequest
* @return A Java Future containing the result of the CreateHyperParameterTuningJob operation returned by the
* service.
* @sample AmazonSageMakerAsync.CreateHyperParameterTuningJob
* @see AWS API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createHyperParameterTuningJobRequest
* @param 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.
* @return A Java Future containing the result of the CreateHyperParameterTuningJob operation returned by the
* service.
* @sample AmazonSageMakerAsyncHandler.CreateHyperParameterTuningJob
* @see AWS API Documentation
*/
java.util.concurrent.Future createHyperParameterTuningJobAsync(
CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createImageRequest
* @return A Java Future containing the result of the CreateImage operation returned by the service.
* @sample AmazonSageMakerAsync.CreateImage
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createImageRequest
* @param 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.
* @return A Java Future containing the result of the CreateImage operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateImage
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createImageAsync(CreateImageRequest createImageRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon
* Container Registry (ECR) container image specified by BaseImage.
*
*
* @param createImageVersionRequest
* @return A Java Future containing the result of the CreateImageVersion operation returned by the service.
* @sample AmazonSageMakerAsync.CreateImageVersion
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createImageVersionRequest
* @param 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.
* @return A Java Future containing the result of the CreateImageVersion operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateImageVersion
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createImageVersionAsync(CreateImageVersionRequest createImageVersionRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createLabelingJobRequest
* @return A Java Future containing the result of the CreateLabelingJob operation returned by the service.
* @sample AmazonSageMakerAsync.CreateLabelingJob
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createLabelingJobRequest
* @param 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.
* @return A Java Future containing the result of the CreateLabelingJob operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateLabelingJob
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createLabelingJobAsync(CreateLabelingJobRequest createLabelingJobRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createModelRequest
* @return A Java Future containing the result of the CreateModel operation returned by the service.
* @sample AmazonSageMakerAsync.CreateModel
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createModelRequest
* @param 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.
* @return A Java Future containing the result of the CreateModel operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateModel
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createModelAsync(CreateModelRequest createModelRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates the definition for a model bias job.
*
*
* @param createModelBiasJobDefinitionRequest
* @return A Java Future containing the result of the CreateModelBiasJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsync.CreateModelBiasJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future createModelBiasJobDefinitionAsync(
CreateModelBiasJobDefinitionRequest createModelBiasJobDefinitionRequest);
/**
*
* Creates the definition for a model bias job.
*
*
* @param createModelBiasJobDefinitionRequest
* @param 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.
* @return A Java Future containing the result of the CreateModelBiasJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsyncHandler.CreateModelBiasJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future createModelBiasJobDefinitionAsync(
CreateModelBiasJobDefinitionRequest createModelBiasJobDefinitionRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates the definition for a model explainability job.
*
*
* @param createModelExplainabilityJobDefinitionRequest
* @return A Java Future containing the result of the CreateModelExplainabilityJobDefinition operation returned by
* the service.
* @sample AmazonSageMakerAsync.CreateModelExplainabilityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future createModelExplainabilityJobDefinitionAsync(
CreateModelExplainabilityJobDefinitionRequest createModelExplainabilityJobDefinitionRequest);
/**
*
* Creates the definition for a model explainability job.
*
*
* @param createModelExplainabilityJobDefinitionRequest
* @param 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.
* @return A Java Future containing the result of the CreateModelExplainabilityJobDefinition operation returned by
* the service.
* @sample AmazonSageMakerAsyncHandler.CreateModelExplainabilityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future createModelExplainabilityJobDefinitionAsync(
CreateModelExplainabilityJobDefinitionRequest createModelExplainabilityJobDefinitionRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
*
*
*
* @param createModelPackageRequest
* @return A Java Future containing the result of the CreateModelPackage operation returned by the service.
* @sample AmazonSageMakerAsync.CreateModelPackage
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
*
*
*
* @param createModelPackageRequest
* @param 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.
* @return A Java Future containing the result of the CreateModelPackage operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateModelPackage
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createModelPackageAsync(CreateModelPackageRequest createModelPackageRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates a model group. A model group contains a group of model versions.
*
*
* @param createModelPackageGroupRequest
* @return A Java Future containing the result of the CreateModelPackageGroup operation returned by the service.
* @sample AmazonSageMakerAsync.CreateModelPackageGroup
* @see AWS API Documentation
*/
java.util.concurrent.Future createModelPackageGroupAsync(CreateModelPackageGroupRequest createModelPackageGroupRequest);
/**
*
* Creates a model group. A model group contains a group of model versions.
*
*
* @param createModelPackageGroupRequest
* @param 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.
* @return A Java Future containing the result of the CreateModelPackageGroup operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateModelPackageGroup
* @see AWS API Documentation
*/
java.util.concurrent.Future createModelPackageGroupAsync(CreateModelPackageGroupRequest createModelPackageGroupRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
*
*
* @param createModelQualityJobDefinitionRequest
* @return A Java Future containing the result of the CreateModelQualityJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsync.CreateModelQualityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createModelQualityJobDefinitionRequest
* @param 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.
* @return A Java Future containing the result of the CreateModelQualityJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsyncHandler.CreateModelQualityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future createModelQualityJobDefinitionAsync(
CreateModelQualityJobDefinitionRequest createModelQualityJobDefinitionRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an
* Amazon SageMaker Endoint.
*
*
* @param createMonitoringScheduleRequest
* @return A Java Future containing the result of the CreateMonitoringSchedule operation returned by the service.
* @sample AmazonSageMakerAsync.CreateMonitoringSchedule
* @see AWS API Documentation
*/
java.util.concurrent.Future createMonitoringScheduleAsync(CreateMonitoringScheduleRequest createMonitoringScheduleRequest);
/**
*
* Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an
* Amazon SageMaker Endoint.
*
*
* @param createMonitoringScheduleRequest
* @param 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.
* @return A Java Future containing the result of the CreateMonitoringSchedule operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateMonitoringSchedule
* @see AWS API Documentation
*/
java.util.concurrent.Future createMonitoringScheduleAsync(CreateMonitoringScheduleRequest createMonitoringScheduleRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createNotebookInstanceRequest
* @return A Java Future containing the result of the CreateNotebookInstance operation returned by the service.
* @sample AmazonSageMakerAsync.CreateNotebookInstance
* @see AWS API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createNotebookInstanceRequest
* @param 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.
* @return A Java Future containing the result of the CreateNotebookInstance operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateNotebookInstance
* @see AWS API Documentation
*/
java.util.concurrent.Future createNotebookInstanceAsync(CreateNotebookInstanceRequest createNotebookInstanceRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createNotebookInstanceLifecycleConfigRequest
* @return A Java Future containing the result of the CreateNotebookInstanceLifecycleConfig operation returned by
* the service.
* @sample AmazonSageMakerAsync.CreateNotebookInstanceLifecycleConfig
* @see AWS API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createNotebookInstanceLifecycleConfigRequest
* @param 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.
* @return A Java Future containing the result of the CreateNotebookInstanceLifecycleConfig operation returned by
* the service.
* @sample AmazonSageMakerAsyncHandler.CreateNotebookInstanceLifecycleConfig
* @see AWS API Documentation
*/
java.util.concurrent.Future createNotebookInstanceLifecycleConfigAsync(
CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates a pipeline using a JSON pipeline definition.
*
*
* @param createPipelineRequest
* @return A Java Future containing the result of the CreatePipeline operation returned by the service.
* @sample AmazonSageMakerAsync.CreatePipeline
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createPipelineAsync(CreatePipelineRequest createPipelineRequest);
/**
*
* Creates a pipeline using a JSON pipeline definition.
*
*
* @param createPipelineRequest
* @param 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.
* @return A Java Future containing the result of the CreatePipeline operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreatePipeline
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createPipelineAsync(CreatePipelineRequest createPipelineRequest,
com.amazonaws.handlers.AsyncHandler 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 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.
*
*
*
* @param createPresignedDomainUrlRequest
* @return A Java Future containing the result of the CreatePresignedDomainUrl operation returned by the service.
* @sample AmazonSageMakerAsync.CreatePresignedDomainUrl
* @see AWS API Documentation
*/
java.util.concurrent.Future 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 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.
*
*
*
* @param createPresignedDomainUrlRequest
* @param 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.
* @return A Java Future containing the result of the CreatePresignedDomainUrl operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreatePresignedDomainUrl
* @see AWS API Documentation
*/
java.util.concurrent.Future createPresignedDomainUrlAsync(CreatePresignedDomainUrlRequest createPresignedDomainUrlRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
*
* @param createPresignedNotebookInstanceUrlRequest
* @return A Java Future containing the result of the CreatePresignedNotebookInstanceUrl operation returned by the
* service.
* @sample AmazonSageMakerAsync.CreatePresignedNotebookInstanceUrl
* @see AWS API Documentation
*/
java.util.concurrent.Future 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.
*
*
*
* @param createPresignedNotebookInstanceUrlRequest
* @param 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.
* @return A Java Future containing the result of the CreatePresignedNotebookInstanceUrl operation returned by the
* service.
* @sample AmazonSageMakerAsyncHandler.CreatePresignedNotebookInstanceUrl
* @see AWS API Documentation
*/
java.util.concurrent.Future createPresignedNotebookInstanceUrlAsync(
CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Creates a processing job.
*
*
* @param createProcessingJobRequest
* @return A Java Future containing the result of the CreateProcessingJob operation returned by the service.
* @sample AmazonSageMakerAsync.CreateProcessingJob
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createProcessingJobAsync(CreateProcessingJobRequest createProcessingJobRequest);
/**
*
* Creates a processing job.
*
*
* @param createProcessingJobRequest
* @param 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.
* @return A Java Future containing the result of the CreateProcessingJob operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateProcessingJob
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createProcessingJobAsync(CreateProcessingJobRequest createProcessingJobRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createProjectRequest
* @return A Java Future containing the result of the CreateProject operation returned by the service.
* @sample AmazonSageMakerAsync.CreateProject
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createProjectRequest
* @param 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.
* @return A Java Future containing the result of the CreateProject operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateProject
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createProjectAsync(CreateProjectRequest createProjectRequest,
com.amazonaws.handlers.AsyncHandler 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 Number (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.
*
*
*
*
* For more information about Amazon SageMaker, see How It Works.
*
*
* @param createTrainingJobRequest
* @return A Java Future containing the result of the CreateTrainingJob operation returned by the service.
* @sample AmazonSageMakerAsync.CreateTrainingJob
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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 Number (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.
*
*
*
*
* For more information about Amazon SageMaker, see How It Works.
*
*
* @param createTrainingJobRequest
* @param 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.
* @return A Java Future containing the result of the CreateTrainingJob operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateTrainingJob
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createTrainingJobAsync(CreateTrainingJobRequest createTrainingJobRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createTransformJobRequest
* @return A Java Future containing the result of the CreateTransformJob operation returned by the service.
* @sample AmazonSageMakerAsync.CreateTransformJob
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createTransformJobRequest
* @param 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.
* @return A Java Future containing the result of the CreateTransformJob operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateTransformJob
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createTransformJobAsync(CreateTransformJobRequest createTransformJobRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createTrialRequest
* @return A Java Future containing the result of the CreateTrial operation returned by the service.
* @sample AmazonSageMakerAsync.CreateTrial
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createTrialRequest
* @param 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.
* @return A Java Future containing the result of the CreateTrial operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateTrial
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createTrialAsync(CreateTrialRequest createTrialRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
*
* @param createTrialComponentRequest
* @return A Java Future containing the result of the CreateTrialComponent operation returned by the service.
* @sample AmazonSageMakerAsync.CreateTrialComponent
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
*
* @param createTrialComponentRequest
* @param 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.
* @return A Java Future containing the result of the CreateTrialComponent operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateTrialComponent
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createTrialComponentAsync(CreateTrialComponentRequest createTrialComponentRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createUserProfileRequest
* @return A Java Future containing the result of the CreateUserProfile operation returned by the service.
* @sample AmazonSageMakerAsync.CreateUserProfile
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createUserProfileRequest
* @param 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.
* @return A Java Future containing the result of the CreateUserProfile operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateUserProfile
* @see AWS
* API Documentation
*/
java.util.concurrent.Future createUserProfileAsync(CreateUserProfileRequest createUserProfileRequest,
com.amazonaws.handlers.AsyncHandler 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).
*
*
* @param createWorkforceRequest
* @return A Java Future containing the result of the CreateWorkforce operation returned by the service.
* @sample AmazonSageMakerAsync.CreateWorkforce
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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).
*
*
* @param createWorkforceRequest
* @param 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.
* @return A Java Future containing the result of the CreateWorkforce operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateWorkforce
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createWorkforceAsync(CreateWorkforceRequest createWorkforceRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createWorkteamRequest
* @return A Java Future containing the result of the CreateWorkteam operation returned by the service.
* @sample AmazonSageMakerAsync.CreateWorkteam
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param createWorkteamRequest
* @param 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.
* @return A Java Future containing the result of the CreateWorkteam operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.CreateWorkteam
* @see AWS API
* Documentation
*/
java.util.concurrent.Future createWorkteamAsync(CreateWorkteamRequest createWorkteamRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes an action.
*
*
* @param deleteActionRequest
* @return A Java Future containing the result of the DeleteAction operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteAction
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteActionAsync(DeleteActionRequest deleteActionRequest);
/**
*
* Deletes an action.
*
*
* @param deleteActionRequest
* @param 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.
* @return A Java Future containing the result of the DeleteAction operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteAction
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteActionAsync(DeleteActionRequest deleteActionRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Removes the specified algorithm from your account.
*
*
* @param deleteAlgorithmRequest
* @return A Java Future containing the result of the DeleteAlgorithm operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteAlgorithm
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteAlgorithmAsync(DeleteAlgorithmRequest deleteAlgorithmRequest);
/**
*
* Removes the specified algorithm from your account.
*
*
* @param deleteAlgorithmRequest
* @param 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.
* @return A Java Future containing the result of the DeleteAlgorithm operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteAlgorithm
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteAlgorithmAsync(DeleteAlgorithmRequest deleteAlgorithmRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Used to stop and delete an app.
*
*
* @param deleteAppRequest
* @return A Java Future containing the result of the DeleteApp operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteApp
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteAppAsync(DeleteAppRequest deleteAppRequest);
/**
*
* Used to stop and delete an app.
*
*
* @param deleteAppRequest
* @param 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.
* @return A Java Future containing the result of the DeleteApp operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteApp
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteAppAsync(DeleteAppRequest deleteAppRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes an AppImageConfig.
*
*
* @param deleteAppImageConfigRequest
* @return A Java Future containing the result of the DeleteAppImageConfig operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteAppImageConfig
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteAppImageConfigAsync(DeleteAppImageConfigRequest deleteAppImageConfigRequest);
/**
*
* Deletes an AppImageConfig.
*
*
* @param deleteAppImageConfigRequest
* @param 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.
* @return A Java Future containing the result of the DeleteAppImageConfig operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteAppImageConfig
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteAppImageConfigAsync(DeleteAppImageConfigRequest deleteAppImageConfigRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes an artifact. Either ArtifactArn or Source must be specified.
*
*
* @param deleteArtifactRequest
* @return A Java Future containing the result of the DeleteArtifact operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteArtifact
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteArtifactAsync(DeleteArtifactRequest deleteArtifactRequest);
/**
*
* Deletes an artifact. Either ArtifactArn or Source must be specified.
*
*
* @param deleteArtifactRequest
* @param 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.
* @return A Java Future containing the result of the DeleteArtifact operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteArtifact
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteArtifactAsync(DeleteArtifactRequest deleteArtifactRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes an association.
*
*
* @param deleteAssociationRequest
* @return A Java Future containing the result of the DeleteAssociation operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteAssociation
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteAssociationAsync(DeleteAssociationRequest deleteAssociationRequest);
/**
*
* Deletes an association.
*
*
* @param deleteAssociationRequest
* @param 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.
* @return A Java Future containing the result of the DeleteAssociation operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteAssociation
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteAssociationAsync(DeleteAssociationRequest deleteAssociationRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes the specified Git repository from your account.
*
*
* @param deleteCodeRepositoryRequest
* @return A Java Future containing the result of the DeleteCodeRepository operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteCodeRepository
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteCodeRepositoryAsync(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest);
/**
*
* Deletes the specified Git repository from your account.
*
*
* @param deleteCodeRepositoryRequest
* @param 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.
* @return A Java Future containing the result of the DeleteCodeRepository operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteCodeRepository
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteCodeRepositoryAsync(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes an context.
*
*
* @param deleteContextRequest
* @return A Java Future containing the result of the DeleteContext operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteContext
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteContextAsync(DeleteContextRequest deleteContextRequest);
/**
*
* Deletes an context.
*
*
* @param deleteContextRequest
* @param 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.
* @return A Java Future containing the result of the DeleteContext operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteContext
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteContextAsync(DeleteContextRequest deleteContextRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes a data quality monitoring job definition.
*
*
* @param deleteDataQualityJobDefinitionRequest
* @return A Java Future containing the result of the DeleteDataQualityJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsync.DeleteDataQualityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteDataQualityJobDefinitionAsync(
DeleteDataQualityJobDefinitionRequest deleteDataQualityJobDefinitionRequest);
/**
*
* Deletes a data quality monitoring job definition.
*
*
* @param deleteDataQualityJobDefinitionRequest
* @param 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.
* @return A Java Future containing the result of the DeleteDataQualityJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsyncHandler.DeleteDataQualityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteDataQualityJobDefinitionAsync(
DeleteDataQualityJobDefinitionRequest deleteDataQualityJobDefinitionRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes a fleet.
*
*
* @param deleteDeviceFleetRequest
* @return A Java Future containing the result of the DeleteDeviceFleet operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteDeviceFleet
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteDeviceFleetAsync(DeleteDeviceFleetRequest deleteDeviceFleetRequest);
/**
*
* Deletes a fleet.
*
*
* @param deleteDeviceFleetRequest
* @param 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.
* @return A Java Future containing the result of the DeleteDeviceFleet operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteDeviceFleet
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteDeviceFleetAsync(DeleteDeviceFleetRequest deleteDeviceFleetRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteDomainRequest
* @return A Java Future containing the result of the DeleteDomain operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteDomain
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteDomainRequest
* @param 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.
* @return A Java Future containing the result of the DeleteDomain operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteDomain
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteDomainAsync(DeleteDomainRequest deleteDomainRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteEndpointRequest
* @return A Java Future containing the result of the DeleteEndpoint operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteEndpoint
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteEndpointRequest
* @param 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.
* @return A Java Future containing the result of the DeleteEndpoint operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteEndpoint
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteEndpointAsync(DeleteEndpointRequest deleteEndpointRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteEndpointConfigRequest
* @return A Java Future containing the result of the DeleteEndpointConfig operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteEndpointConfig
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteEndpointConfigRequest
* @param 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.
* @return A Java Future containing the result of the DeleteEndpointConfig operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteEndpointConfig
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteEndpointConfigAsync(DeleteEndpointConfigRequest deleteEndpointConfigRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteExperimentRequest
* @return A Java Future containing the result of the DeleteExperiment operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteExperiment
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteExperimentRequest
* @param 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.
* @return A Java Future containing the result of the DeleteExperiment operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteExperiment
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteExperimentAsync(DeleteExperimentRequest deleteExperimentRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteFeatureGroupRequest
* @return A Java Future containing the result of the DeleteFeatureGroup operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteFeatureGroup
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteFeatureGroupRequest
* @param 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.
* @return A Java Future containing the result of the DeleteFeatureGroup operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteFeatureGroup
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteFeatureGroupAsync(DeleteFeatureGroupRequest deleteFeatureGroupRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes the specified flow definition.
*
*
* @param deleteFlowDefinitionRequest
* @return A Java Future containing the result of the DeleteFlowDefinition operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteFlowDefinition
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteFlowDefinitionAsync(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest);
/**
*
* Deletes the specified flow definition.
*
*
* @param deleteFlowDefinitionRequest
* @param 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.
* @return A Java Future containing the result of the DeleteFlowDefinition operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteFlowDefinition
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteFlowDefinitionAsync(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteHumanTaskUiRequest
* @return A Java Future containing the result of the DeleteHumanTaskUi operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteHumanTaskUi
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteHumanTaskUiRequest
* @param 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.
* @return A Java Future containing the result of the DeleteHumanTaskUi operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteHumanTaskUi
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteHumanTaskUiAsync(DeleteHumanTaskUiRequest deleteHumanTaskUiRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
*
*
* @param deleteImageRequest
* @return A Java Future containing the result of the DeleteImage operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteImage
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteImageAsync(DeleteImageRequest deleteImageRequest);
/**
*
* Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
*
*
* @param deleteImageRequest
* @param 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.
* @return A Java Future containing the result of the DeleteImage operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteImage
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteImageAsync(DeleteImageRequest deleteImageRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
*
*
* @param deleteImageVersionRequest
* @return A Java Future containing the result of the DeleteImageVersion operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteImageVersion
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteImageVersionAsync(DeleteImageVersionRequest deleteImageVersionRequest);
/**
*
* Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
*
*
* @param deleteImageVersionRequest
* @param 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.
* @return A Java Future containing the result of the DeleteImageVersion operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteImageVersion
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteImageVersionAsync(DeleteImageVersionRequest deleteImageVersionRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteModelRequest
* @return A Java Future containing the result of the DeleteModel operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteModel
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteModelRequest
* @param 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.
* @return A Java Future containing the result of the DeleteModel operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteModel
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteModelAsync(DeleteModelRequest deleteModelRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes an Amazon SageMaker model bias job definition.
*
*
* @param deleteModelBiasJobDefinitionRequest
* @return A Java Future containing the result of the DeleteModelBiasJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsync.DeleteModelBiasJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteModelBiasJobDefinitionAsync(
DeleteModelBiasJobDefinitionRequest deleteModelBiasJobDefinitionRequest);
/**
*
* Deletes an Amazon SageMaker model bias job definition.
*
*
* @param deleteModelBiasJobDefinitionRequest
* @param 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.
* @return A Java Future containing the result of the DeleteModelBiasJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsyncHandler.DeleteModelBiasJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteModelBiasJobDefinitionAsync(
DeleteModelBiasJobDefinitionRequest deleteModelBiasJobDefinitionRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes an Amazon SageMaker model explainability job definition.
*
*
* @param deleteModelExplainabilityJobDefinitionRequest
* @return A Java Future containing the result of the DeleteModelExplainabilityJobDefinition operation returned by
* the service.
* @sample AmazonSageMakerAsync.DeleteModelExplainabilityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteModelExplainabilityJobDefinitionAsync(
DeleteModelExplainabilityJobDefinitionRequest deleteModelExplainabilityJobDefinitionRequest);
/**
*
* Deletes an Amazon SageMaker model explainability job definition.
*
*
* @param deleteModelExplainabilityJobDefinitionRequest
* @param 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.
* @return A Java Future containing the result of the DeleteModelExplainabilityJobDefinition operation returned by
* the service.
* @sample AmazonSageMakerAsyncHandler.DeleteModelExplainabilityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteModelExplainabilityJobDefinitionAsync(
DeleteModelExplainabilityJobDefinitionRequest deleteModelExplainabilityJobDefinitionRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteModelPackageRequest
* @return A Java Future containing the result of the DeleteModelPackage operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteModelPackage
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteModelPackageRequest
* @param 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.
* @return A Java Future containing the result of the DeleteModelPackage operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteModelPackage
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteModelPackageAsync(DeleteModelPackageRequest deleteModelPackageRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes the specified model group.
*
*
* @param deleteModelPackageGroupRequest
* @return A Java Future containing the result of the DeleteModelPackageGroup operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteModelPackageGroup
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteModelPackageGroupAsync(DeleteModelPackageGroupRequest deleteModelPackageGroupRequest);
/**
*
* Deletes the specified model group.
*
*
* @param deleteModelPackageGroupRequest
* @param 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.
* @return A Java Future containing the result of the DeleteModelPackageGroup operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteModelPackageGroup
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteModelPackageGroupAsync(DeleteModelPackageGroupRequest deleteModelPackageGroupRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes a model group resource policy.
*
*
* @param deleteModelPackageGroupPolicyRequest
* @return A Java Future containing the result of the DeleteModelPackageGroupPolicy operation returned by the
* service.
* @sample AmazonSageMakerAsync.DeleteModelPackageGroupPolicy
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteModelPackageGroupPolicyAsync(
DeleteModelPackageGroupPolicyRequest deleteModelPackageGroupPolicyRequest);
/**
*
* Deletes a model group resource policy.
*
*
* @param deleteModelPackageGroupPolicyRequest
* @param 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.
* @return A Java Future containing the result of the DeleteModelPackageGroupPolicy operation returned by the
* service.
* @sample AmazonSageMakerAsyncHandler.DeleteModelPackageGroupPolicy
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteModelPackageGroupPolicyAsync(
DeleteModelPackageGroupPolicyRequest deleteModelPackageGroupPolicyRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes the secified model quality monitoring job definition.
*
*
* @param deleteModelQualityJobDefinitionRequest
* @return A Java Future containing the result of the DeleteModelQualityJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsync.DeleteModelQualityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteModelQualityJobDefinitionAsync(
DeleteModelQualityJobDefinitionRequest deleteModelQualityJobDefinitionRequest);
/**
*
* Deletes the secified model quality monitoring job definition.
*
*
* @param deleteModelQualityJobDefinitionRequest
* @param 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.
* @return A Java Future containing the result of the DeleteModelQualityJobDefinition operation returned by the
* service.
* @sample AmazonSageMakerAsyncHandler.DeleteModelQualityJobDefinition
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteModelQualityJobDefinitionAsync(
DeleteModelQualityJobDefinitionRequest deleteModelQualityJobDefinitionRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteMonitoringScheduleRequest
* @return A Java Future containing the result of the DeleteMonitoringSchedule operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteMonitoringSchedule
* @see AWS API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteMonitoringScheduleRequest
* @param 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.
* @return A Java Future containing the result of the DeleteMonitoringSchedule operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteMonitoringSchedule
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteMonitoringScheduleAsync(DeleteMonitoringScheduleRequest deleteMonitoringScheduleRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
*
* @param deleteNotebookInstanceRequest
* @return A Java Future containing the result of the DeleteNotebookInstance operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteNotebookInstance
* @see AWS API Documentation
*/
java.util.concurrent.Future 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.
*
*
*
* @param deleteNotebookInstanceRequest
* @param 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.
* @return A Java Future containing the result of the DeleteNotebookInstance operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteNotebookInstance
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteNotebookInstanceAsync(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes a notebook instance lifecycle configuration.
*
*
* @param deleteNotebookInstanceLifecycleConfigRequest
* @return A Java Future containing the result of the DeleteNotebookInstanceLifecycleConfig operation returned by
* the service.
* @sample AmazonSageMakerAsync.DeleteNotebookInstanceLifecycleConfig
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteNotebookInstanceLifecycleConfigAsync(
DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest);
/**
*
* Deletes a notebook instance lifecycle configuration.
*
*
* @param deleteNotebookInstanceLifecycleConfigRequest
* @param 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.
* @return A Java Future containing the result of the DeleteNotebookInstanceLifecycleConfig operation returned by
* the service.
* @sample AmazonSageMakerAsyncHandler.DeleteNotebookInstanceLifecycleConfig
* @see AWS API Documentation
*/
java.util.concurrent.Future deleteNotebookInstanceLifecycleConfigAsync(
DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes a pipeline if there are no in-progress executions.
*
*
* @param deletePipelineRequest
* @return A Java Future containing the result of the DeletePipeline operation returned by the service.
* @sample AmazonSageMakerAsync.DeletePipeline
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deletePipelineAsync(DeletePipelineRequest deletePipelineRequest);
/**
*
* Deletes a pipeline if there are no in-progress executions.
*
*
* @param deletePipelineRequest
* @param 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.
* @return A Java Future containing the result of the DeletePipeline operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeletePipeline
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deletePipelineAsync(DeletePipelineRequest deletePipelineRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Delete the specified project.
*
*
* @param deleteProjectRequest
* @return A Java Future containing the result of the DeleteProject operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteProject
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteProjectAsync(DeleteProjectRequest deleteProjectRequest);
/**
*
* Delete the specified project.
*
*
* @param deleteProjectRequest
* @param 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.
* @return A Java Future containing the result of the DeleteProject operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteProject
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteProjectAsync(DeleteProjectRequest deleteProjectRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
*
* @param deleteTagsRequest
* @return A Java Future containing the result of the DeleteTags operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteTags
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
*
* @param deleteTagsRequest
* @param 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.
* @return A Java Future containing the result of the DeleteTags operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteTags
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteTagsAsync(DeleteTagsRequest deleteTagsRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteTrialRequest
* @return A Java Future containing the result of the DeleteTrial operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteTrial
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteTrialRequest
* @param 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.
* @return A Java Future containing the result of the DeleteTrial operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteTrial
* @see AWS API
* Documentation
*/
java.util.concurrent.Future deleteTrialAsync(DeleteTrialRequest deleteTrialRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteTrialComponentRequest
* @return A Java Future containing the result of the DeleteTrialComponent operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteTrialComponent
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteTrialComponentRequest
* @param 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.
* @return A Java Future containing the result of the DeleteTrialComponent operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteTrialComponent
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteTrialComponentAsync(DeleteTrialComponentRequest deleteTrialComponentRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteUserProfileRequest
* @return A Java Future containing the result of the DeleteUserProfile operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteUserProfile
* @see AWS
* API Documentation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteUserProfileRequest
* @param 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.
* @return A Java Future containing the result of the DeleteUserProfile operation returned by the service.
* @sample AmazonSageMakerAsyncHandler.DeleteUserProfile
* @see AWS
* API Documentation
*/
java.util.concurrent.Future deleteUserProfileAsync(DeleteUserProfileRequest deleteUserProfileRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
*
* @param deleteWorkforceRequest
* @return A Java Future containing the result of the DeleteWorkforce operation returned by the service.
* @sample AmazonSageMakerAsync.DeleteWorkforce
* @see AWS API
* Documentation
*/
java.util.concurrent.Future 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