*
* Provides APIs for creating and managing SageMaker resources. *
** Other Resources: *
** All service calls made using this new client object are blocking, and will not return until the service call * completes. * * @param clientParams * Object providing client parameters. */ AmazonSageMakerClient(AwsSyncClientParams clientParams) { this(clientParams, false); } /** * Constructs a new client to invoke service methods on SageMaker using the specified parameters. * *
* All service calls made using this new client object are blocking, and will not return until the service call * completes. * * @param clientParams * Object providing client parameters. */ AmazonSageMakerClient(AwsSyncClientParams clientParams, boolean endpointDiscoveryEnabled) { super(clientParams); this.awsCredentialsProvider = clientParams.getCredentialsProvider(); this.advancedConfig = clientParams.getAdvancedConfig(); init(); } private void init() { setServiceNameIntern(DEFAULT_SIGNING_NAME); setEndpointPrefix(ENDPOINT_PREFIX); // calling this.setEndPoint(...) will also modify the signer accordingly setEndpoint("sagemaker.us-east-1.amazonaws.com"); HandlerChainFactory chainFactory = new HandlerChainFactory(); requestHandler2s.addAll(chainFactory.newRequestHandlerChain("/com/amazonaws/services/sagemaker/request.handlers")); requestHandler2s.addAll(chainFactory.newRequestHandler2Chain("/com/amazonaws/services/sagemaker/request.handler2s")); requestHandler2s.addAll(chainFactory.getGlobalHandlers()); } /** *
* 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 Result of the AddAssociation operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.AddAssociation * @see AWS API * Documentation */ @Override public AddAssociationResult addAssociation(AddAssociationRequest request) { request = beforeClientExecution(request); return executeAddAssociation(request); } @SdkInternalApi final AddAssociationResult executeAddAssociation(AddAssociationRequest addAssociationRequest) { ExecutionContext executionContext = createExecutionContext(addAssociationRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, * training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint * configurations, and endpoints. *
** Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information * about tags, see For more information, see Amazon Web Services Tagging * Strategies. *
*
* Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the
* hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter
* tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter
* tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you
* first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob
*
* Tags that you add to a SageMaker Studio Domain or User Profile by calling this API are also added to any Apps
* that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile
* launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also
* added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User
* Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.
*
* 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 Result of the AssociateTrialComponent operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.AssociateTrialComponent * @see AWS API Documentation */ @Override public AssociateTrialComponentResult associateTrialComponent(AssociateTrialComponentRequest request) { request = beforeClientExecution(request); return executeAssociateTrialComponent(request); } @SdkInternalApi final AssociateTrialComponentResult executeAssociateTrialComponent(AssociateTrialComponentRequest associateTrialComponentRequest) { ExecutionContext executionContext = createExecutionContext(associateTrialComponentRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* This action batch describes a list of versioned model packages *
* * @param batchDescribeModelPackageRequest * @return Result of the BatchDescribeModelPackage operation returned by the service. * @sample AmazonSageMaker.BatchDescribeModelPackage * @see AWS API Documentation */ @Override public BatchDescribeModelPackageResult batchDescribeModelPackage(BatchDescribeModelPackageRequest request) { request = beforeClientExecution(request); return executeBatchDescribeModelPackage(request); } @SdkInternalApi final BatchDescribeModelPackageResult executeBatchDescribeModelPackage(BatchDescribeModelPackageRequest batchDescribeModelPackageRequest) { ExecutionContext executionContext = createExecutionContext(batchDescribeModelPackageRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates an action. An action is a lineage tracking entity that represents an action or activity. For * example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. * For more information, see Amazon * SageMaker ML Lineage Tracking. *
* * @param createActionRequest * @return Result of the CreateAction operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateAction * @see AWS API * Documentation */ @Override public CreateActionResult createAction(CreateActionRequest request) { request = beforeClientExecution(request); return executeCreateAction(request); } @SdkInternalApi final CreateActionResult executeCreateAction(CreateActionRequest createActionRequest) { ExecutionContext executionContext = createExecutionContext(createActionRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services * Marketplace. *
* * @param createAlgorithmRequest * @return Result of the CreateAlgorithm operation returned by the service. * @sample AmazonSageMaker.CreateAlgorithm * @see AWS API * Documentation */ @Override public CreateAlgorithmResult createAlgorithm(CreateAlgorithmRequest request) { request = beforeClientExecution(request); return executeCreateAlgorithm(request); } @SdkInternalApi final CreateAlgorithmResult executeCreateAlgorithm(CreateAlgorithmRequest createAlgorithmRequest) { ExecutionContext executionContext = createExecutionContext(createAlgorithmRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a running app for the specified UserProfile. 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 Result of the CreateApp operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateApp * @see AWS API * Documentation */ @Override public CreateAppResult createApp(CreateAppRequest request) { request = beforeClientExecution(request); return executeCreateApp(request); } @SdkInternalApi final CreateAppResult executeCreateApp(CreateAppRequest createAppRequest) { ExecutionContext executionContext = createExecutionContext(createAppRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the * Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image. *
* * @param createAppImageConfigRequest * @return Result of the CreateAppImageConfig operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateAppImageConfig * @see AWS * API Documentation */ @Override public CreateAppImageConfigResult createAppImageConfig(CreateAppImageConfigRequest request) { request = beforeClientExecution(request); return executeCreateAppImageConfig(request); } @SdkInternalApi final CreateAppImageConfigResult executeCreateAppImageConfig(CreateAppImageConfigRequest createAppImageConfigRequest) { ExecutionContext executionContext = createExecutionContext(createAppImageConfigRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or * data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see * Amazon SageMaker ML Lineage * Tracking. *
* * @param createArtifactRequest * @return Result of the CreateArtifact operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateArtifact * @see AWS API * Documentation */ @Override public CreateArtifactResult createArtifact(CreateArtifactRequest request) { request = beforeClientExecution(request); return executeCreateArtifact(request); } @SdkInternalApi final CreateArtifactResult executeCreateArtifact(CreateArtifactRequest createArtifactRequest) { ExecutionContext executionContext = createExecutionContext(createArtifactRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates an Autopilot job also referred to as Autopilot experiment or AutoML job. *
** We recommend using the new versions CreateAutoMLJobV2 * and * DescribeAutoMLJobV2, which offer backward compatibility. *
*
* CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version
* CreateAutoMLJob, as well as non-tabular problem types such as image or text classification.
*
* Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
*
* You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob. *
* * @param createAutoMLJobRequest * @return Result of the CreateAutoMLJob operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateAutoMLJob * @see AWS API * Documentation */ @Override public CreateAutoMLJobResult createAutoMLJob(CreateAutoMLJobRequest request) { request = beforeClientExecution(request); return executeCreateAutoMLJob(request); } @SdkInternalApi final CreateAutoMLJobResult executeCreateAutoMLJob(CreateAutoMLJobRequest createAutoMLJobRequest) { ExecutionContext executionContext = createExecutionContext(createAutoMLJobRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2. *
** CreateAutoMLJobV2 * and * DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and * DescribeAutoMLJob * which offer backward compatibility. *
*
* CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version
* CreateAutoMLJob, as well as non-tabular problem types such as image or text classification.
*
* Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
*
* For the list of available problem types supported by CreateAutoMLJobV2, see AutoMLProblemTypeConfig.
*
* You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2. *
* * @param createAutoMLJobV2Request * @return Result of the CreateAutoMLJobV2 operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateAutoMLJobV2 * @see AWS * API Documentation */ @Override public CreateAutoMLJobV2Result createAutoMLJobV2(CreateAutoMLJobV2Request request) { request = beforeClientExecution(request); return executeCreateAutoMLJobV2(request); } @SdkInternalApi final CreateAutoMLJobV2Result executeCreateAutoMLJobV2(CreateAutoMLJobV2Request createAutoMLJobV2Request) { ExecutionContext executionContext = createExecutionContext(createAutoMLJobV2Request); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook * instances so that you can use Git source control for the notebooks you create. The Git repository is a resource * in your SageMaker account, so it can be associated with more than one notebook instance, and it persists * independently from the lifecycle of any notebook instances it is associated with. *
** The repository can be hosted either in Amazon Web Services CodeCommit or * in any other Git repository. *
* * @param createCodeRepositoryRequest * @return Result of the CreateCodeRepository operation returned by the service. * @sample AmazonSageMaker.CreateCodeRepository * @see AWS * API Documentation */ @Override public CreateCodeRepositoryResult createCodeRepository(CreateCodeRepositoryRequest request) { request = beforeClientExecution(request); return executeCreateCodeRepository(request); } @SdkInternalApi final CreateCodeRepositoryResult executeCreateCodeRepository(CreateCodeRepositoryRequest createCodeRepositoryRequest) { ExecutionContext executionContext = createExecutionContext(createCodeRepositoryRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model * artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify. *
** If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model * artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that * case, deploy them as an ML resource. *
** In the request body, you provide the following: *
** A name for the compilation job *
** Information about the input model artifacts *
** The output location for the compiled model and the device (target) that the model runs on *
** The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation * job. *
*
* You can also provide a Tag to track the model compilation job's resource use and costs. The response
* body contains 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 Result of the CreateCompilationJob operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateCompilationJob * @see AWS * API Documentation */ @Override public CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest request) { request = beforeClientExecution(request); return executeCreateCompilationJob(request); } @SdkInternalApi final CreateCompilationJobResult executeCreateCompilationJob(CreateCompilationJobRequest createCompilationJobRequest) { ExecutionContext executionContext = createExecutionContext(createCompilationJobRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a context. A context is a lineage tracking entity that represents a logical grouping of other * tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage * Tracking. *
* * @param createContextRequest * @return Result of the CreateContext operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateContext * @see AWS API * Documentation */ @Override public CreateContextResult createContext(CreateContextRequest request) { request = beforeClientExecution(request); return executeCreateContext(request); } @SdkInternalApi final CreateContextResult executeCreateContext(CreateContextRequest createContextRequest) { ExecutionContext executionContext = createExecutionContext(createContextRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* 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 Result of the CreateDataQualityJobDefinition operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateDataQualityJobDefinition * @see AWS API Documentation */ @Override public CreateDataQualityJobDefinitionResult createDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest request) { request = beforeClientExecution(request); return executeCreateDataQualityJobDefinition(request); } @SdkInternalApi final CreateDataQualityJobDefinitionResult executeCreateDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest createDataQualityJobDefinitionRequest) { ExecutionContext executionContext = createExecutionContext(createDataQualityJobDefinitionRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a device fleet. *
* * @param createDeviceFleetRequest * @return Result of the CreateDeviceFleet operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateDeviceFleet * @see AWS * API Documentation */ @Override public CreateDeviceFleetResult createDeviceFleet(CreateDeviceFleetRequest request) { request = beforeClientExecution(request); return executeCreateDeviceFleet(request); } @SdkInternalApi final CreateDeviceFleetResult executeCreateDeviceFleet(CreateDeviceFleetRequest createDeviceFleetRequest) { ExecutionContext executionContext = createExecutionContext(createDeviceFleetRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request
* Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic
* File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon
* Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts
* with each other.
*
* EFS storage *
** When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user * receives a private home directory within the EFS volume for notebooks, Git repositories, and data files. *
** SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume * attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a * customer managed key. For more information, see Protect Data at Rest Using * Encryption. *
** VPC configuration *
*
* All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For
* other Studio traffic, you can specify the AppNetworkAccessType parameter.
* AppNetworkAccessType corresponds to the network access type that you choose when you onboard to
* Studio. The following options are available:
*
* PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows
* internet access. This is the default value.
*
* VpcOnly - All Studio traffic is through the specified VPC and subnets. Internet access is disabled
* by default. To allow internet access, you must specify a NAT gateway.
*
* When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless * your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups * allow outbound connections. *
** NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a * SageMaker Studio app successfully. *
** For more information, see Connect * SageMaker Studio Notebooks to Resources in a VPC. *
* * @param createDomainRequest * @return Result of the CreateDomain operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateDomain * @see AWS API * Documentation */ @Override public CreateDomainResult createDomain(CreateDomainRequest request) { request = beforeClientExecution(request); return executeCreateDomain(request); } @SdkInternalApi final CreateDomainResult executeCreateDomain(CreateDomainRequest createDomainRequest) { ExecutionContext executionContext = createExecutionContext(createDomainRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment * configuration and devices. *
* * @param createEdgeDeploymentPlanRequest * @return Result of the CreateEdgeDeploymentPlan operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateEdgeDeploymentPlan * @see AWS API Documentation */ @Override public CreateEdgeDeploymentPlanResult createEdgeDeploymentPlan(CreateEdgeDeploymentPlanRequest request) { request = beforeClientExecution(request); return executeCreateEdgeDeploymentPlan(request); } @SdkInternalApi final CreateEdgeDeploymentPlanResult executeCreateEdgeDeploymentPlan(CreateEdgeDeploymentPlanRequest createEdgeDeploymentPlanRequest) { ExecutionContext executionContext = createExecutionContext(createEdgeDeploymentPlanRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a new stage in an existing edge deployment plan. *
* * @param createEdgeDeploymentStageRequest * @return Result of the CreateEdgeDeploymentStage operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateEdgeDeploymentStage * @see AWS API Documentation */ @Override public CreateEdgeDeploymentStageResult createEdgeDeploymentStage(CreateEdgeDeploymentStageRequest request) { request = beforeClientExecution(request); return executeCreateEdgeDeploymentStage(request); } @SdkInternalApi final CreateEdgeDeploymentStageResult executeCreateEdgeDeploymentStage(CreateEdgeDeploymentStageRequest createEdgeDeploymentStageRequest) { ExecutionContext executionContext = createExecutionContext(createEdgeDeploymentStageRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon * Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the * resulting artifacts to an S3 bucket that you specify. *
* * @param createEdgePackagingJobRequest * @return Result of the CreateEdgePackagingJob operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateEdgePackagingJob * @see AWS API Documentation */ @Override public CreateEdgePackagingJobResult createEdgePackagingJob(CreateEdgePackagingJobRequest request) { request = beforeClientExecution(request); return executeCreateEdgePackagingJob(request); } @SdkInternalApi final CreateEdgePackagingJobResult executeCreateEdgePackagingJob(CreateEdgePackagingJobRequest createEdgePackagingJobRequest) { ExecutionContext executionContext = createExecutionContext(createEdgePackagingJobRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to * provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API. *
** Use this API to deploy models using SageMaker hosting services. *
** For an example that calls this method when deploying a model to SageMaker hosting services, see the Create Endpoint example notebook. *
*
* You must not delete an EndpointConfig that is in use by an endpoint that is live or while the
* UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To
* update an endpoint, you must create a new EndpointConfig.
*
* The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account. *
** When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and * deploys the model(s) on them. *
*
* When you call CreateEndpoint, a
* load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a
* DynamoDB table supporting
* Eventually Consistent Reads , the response might not reflect the results of a recently completed
* write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
* this causes a validation error. If you repeat your read request after a short time, the response should return
* the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
* call
* DescribeEndpointConfig before calling CreateEndpoint to
* minimize the potential impact of a DynamoDB eventually consistent read.
*
* When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the
* endpoint, it sets the status to InService. SageMaker can then process incoming requests for
* inferences. To check the status of an endpoint, use the DescribeEndpoint
* API.
*
* If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web * Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services * STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web * Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, * see Activating * and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services * Identity and Access Management User Guide. *
** To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search * the IAM role that you want to grant access to use the CreateEndpoint and * * CreateEndpointConfig API operations, add the following policies to the role. *
*
* Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.
*
* Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the * JSON file of the IAM role: *
*
* "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]
*
* "Resource": [
*
* "arn:aws:sagemaker:region:account-id:endpoint/endpointName"
*
* "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
*
* ]
*
* For more information, see SageMaker API Permissions: * Actions, Permissions, and Resources Reference. *
*
* Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration,
* you identify one or more models, created using the CreateModel API, to deploy and the resources that
* you want SageMaker to provision. Then you call the CreateEndpoint API.
*
* Use this API if you want to use SageMaker hosting services to deploy models into production. *
*
* In the request, you define a ProductionVariant, for each model that you want to deploy. Each
* ProductionVariant parameter also describes the resources that you want SageMaker to provision. This
* includes the number and type of ML compute instances to deploy.
*
* If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you
* want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign
* traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and
* one-third to model B.
*
* When you call CreateEndpoint, a
* load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a
* DynamoDB table supporting
* Eventually Consistent Reads , the response might not reflect the results of a recently completed
* write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
* this causes a validation error. If you repeat your read request after a short time, the response should return
* the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
* call
* DescribeEndpointConfig before calling CreateEndpoint to
* minimize the potential impact of a DynamoDB eventually consistent read.
*
* Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared * and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine * learning model. *
** In the Studio UI, trials are referred to as run groups and trial components are referred to as * runs. *
** The goal of an experiment is to determine the components that produce the best model. Multiple trials are * performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the * remaining inputs constant. *
** When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are * automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must * use the logging APIs provided by the SDK. *
** You can add tags to experiments, trials, trial components and then use the Search API to search for the * tags. *
*
* To add a description to an experiment, specify the optional Description parameter. To add a
* description later, or to change the description, call the UpdateExperiment
* API.
*
* To get a list of all your experiments, call the ListExperiments * API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To * create a trial call the CreateTrial API. *
* * @param createExperimentRequest * @return Result of the CreateExperiment operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateExperiment * @see AWS API * Documentation */ @Override public CreateExperimentResult createExperiment(CreateExperimentRequest request) { request = beforeClientExecution(request); return executeCreateExperiment(request); } @SdkInternalApi final CreateExperimentResult executeCreateExperiment(CreateExperimentRequest createExperimentRequest) { ExecutionContext executionContext = createExecutionContext(createExperimentRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request
* Create a new FeatureGroup. A FeatureGroup is a group of Features defined
* in the FeatureStore to describe a Record.
*
* The FeatureGroup defines the schema and features contained in the FeatureGroup. A
* FeatureGroup definition is composed of a list of Features, a
* RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its
* OnlineStore and OfflineStore. Check Amazon Web Services service
* quotas to see the FeatureGroups quota for your Amazon Web Services account.
*
* You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a
* FeatureGroup.
*
* Creates a flow definition. *
* * @param createFlowDefinitionRequest * @return Result of the CreateFlowDefinition operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateFlowDefinition * @see AWS * API Documentation */ @Override public CreateFlowDefinitionResult createFlowDefinition(CreateFlowDefinitionRequest request) { request = beforeClientExecution(request); return executeCreateFlowDefinition(request); } @SdkInternalApi final CreateFlowDefinitionResult executeCreateFlowDefinition(CreateFlowDefinitionRequest createFlowDefinitionRequest) { ExecutionContext executionContext = createExecutionContext(createFlowDefinitionRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Create a hub. *
** Hub APIs are only callable through SageMaker Studio. *
** 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 Result of the CreateHumanTaskUi operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateHumanTaskUi * @see AWS * API Documentation */ @Override public CreateHumanTaskUiResult createHumanTaskUi(CreateHumanTaskUiRequest request) { request = beforeClientExecution(request); return executeCreateHumanTaskUi(request); } @SdkInternalApi final CreateHumanTaskUiResult executeCreateHumanTaskUi(CreateHumanTaskUiRequest createHumanTaskUiRequest) { ExecutionContext executionContext = createExecutionContext(createHumanTaskUiRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many * training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that * you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured * by an objective metric that you choose. *
** A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for * each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see * View * Experiments, Trials, and Trial Components. *
** Do not include any security-sensitive information including account access IDs, secrets or tokens in any * hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your * training job request and return an exception error. *
** Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a * container image stored in Amazon Elastic Container Registry (ECR). For more information, see Bring your own SageMaker image. *
* * @param createImageRequest * @return Result of the CreateImage operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateImage * @see AWS API * Documentation */ @Override public CreateImageResult createImage(CreateImageRequest request) { request = beforeClientExecution(request); return executeCreateImage(request); } @SdkInternalApi final CreateImageResult executeCreateImage(CreateImageRequest createImageRequest) { ExecutionContext executionContext = createExecutionContext(createImageRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request
* Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon
* Elastic Container Registry (ECR) container image specified by BaseImage.
*
* Creates an inference experiment using the configurations specified in the request. *
** Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference * endpoint. For more information about inference experiments, see Shadow tests. *
** Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model * variants based on your specified configuration. *
** While the experiment is in progress or after it has concluded, you can view metrics that compare your model * variants. For more information, see View, monitor, and * edit shadow tests. *
* * @param createInferenceExperimentRequest * @return Result of the CreateInferenceExperiment operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateInferenceExperiment * @see AWS API Documentation */ @Override public CreateInferenceExperimentResult createInferenceExperiment(CreateInferenceExperimentRequest request) { request = beforeClientExecution(request); return executeCreateInferenceExperiment(request); } @SdkInternalApi final CreateInferenceExperimentResult executeCreateInferenceExperiment(CreateInferenceExperimentRequest createInferenceExperimentRequest) { ExecutionContext executionContext = createExecutionContext(createInferenceExperimentRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Starts a recommendation job. You can create either an instance recommendation or load test job. *
* * @param createInferenceRecommendationsJobRequest * @return Result of the CreateInferenceRecommendationsJob operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateInferenceRecommendationsJob * @see AWS API Documentation */ @Override public CreateInferenceRecommendationsJobResult createInferenceRecommendationsJob(CreateInferenceRecommendationsJobRequest request) { request = beforeClientExecution(request); return executeCreateInferenceRecommendationsJob(request); } @SdkInternalApi final CreateInferenceRecommendationsJobResult executeCreateInferenceRecommendationsJob( CreateInferenceRecommendationsJobRequest createInferenceRecommendationsJobRequest) { ExecutionContext executionContext = createExecutionContext(createInferenceRecommendationsJobRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to * train machine learning models. *
** You can select your workforce from one of three providers: *
** A private workforce that you create. It can include employees, contractors, and outside experts. Use a private * workforce when want the data to stay within your organization or when a specific set of skills is required. *
** One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in * specific areas. *
** The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data * or data that has been stripped of any personally identifiable information. *
** You can also use automated data labeling to reduce the number of data objects that need to be labeled by a * human. Automated data labeling uses active learning to determine if a data object can be labeled by * machine or if it needs to be sent to a human worker. For more information, see Using Automated Data * Labeling. *
** The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that * describes the location of each object. For more information, see Using Input and Output Data. *
** The output can be used as the manifest file for another labeling job or as training data for your machine * learning models. *
*
* You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job
* stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled.
* A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send
* new data objects to an active (InProgress) streaming labeling job in real time. To learn how to
* create a static labeling job, see Create a Labeling Job
* (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling
* Job.
*
* Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the * primary container, you specify the Docker image that contains inference code, artifacts (from prior training), * and a custom environment map that the inference code uses when you deploy the model for predictions. *
** Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job. *
*
* To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then
* create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that
* you defined for the model in the hosting environment.
*
* For an example that calls this method when deploying a model to SageMaker hosting services, see Create a Model (Amazon Web Services SDK for Python (Boto 3)). *
*
* To run a batch transform using your model, you start a job with the CreateTransformJob API.
* SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
*
* In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image * for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM * role to manage permissions the inference code needs. For example, if the inference code access any other Amazon * Web Services resources, you grant necessary permissions via this role. *
* * @param createModelRequest * @return Result of the CreateModel operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateModel * @see AWS API * Documentation */ @Override public CreateModelResult createModel(CreateModelRequest request) { request = beforeClientExecution(request); return executeCreateModel(request); } @SdkInternalApi final CreateModelResult executeCreateModel(CreateModelRequest createModelRequest) { ExecutionContext executionContext = createExecutionContext(createModelRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates the definition for a model bias job. *
* * @param createModelBiasJobDefinitionRequest * @return Result of the CreateModelBiasJobDefinition operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateModelBiasJobDefinition * @see AWS API Documentation */ @Override public CreateModelBiasJobDefinitionResult createModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest request) { request = beforeClientExecution(request); return executeCreateModelBiasJobDefinition(request); } @SdkInternalApi final CreateModelBiasJobDefinitionResult executeCreateModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest createModelBiasJobDefinitionRequest) { ExecutionContext executionContext = createExecutionContext(createModelBiasJobDefinitionRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates an Amazon SageMaker Model Card. *
** For information about how to use model cards, see Amazon SageMaker Model Card. *
* * @param createModelCardRequest * @return Result of the CreateModelCard operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @sample AmazonSageMaker.CreateModelCard
* @see AWS API
* Documentation
*/
@Override
public CreateModelCardResult createModelCard(CreateModelCardRequest request) {
request = beforeClientExecution(request);
return executeCreateModelCard(request);
}
@SdkInternalApi
final CreateModelCardResult executeCreateModelCard(CreateModelCardRequest createModelCardRequest) {
ExecutionContext executionContext = createExecutionContext(createModelCardRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request* Creates an Amazon SageMaker Model Card export job. *
* * @param createModelCardExportJobRequest * @return Result of the CreateModelCardExportJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @sample AmazonSageMaker.CreateModelCardExportJob
* @see AWS API Documentation
*/
@Override
public CreateModelCardExportJobResult createModelCardExportJob(CreateModelCardExportJobRequest request) {
request = beforeClientExecution(request);
return executeCreateModelCardExportJob(request);
}
@SdkInternalApi
final CreateModelCardExportJobResult executeCreateModelCardExportJob(CreateModelCardExportJobRequest createModelCardExportJobRequest) {
ExecutionContext executionContext = createExecutionContext(createModelCardExportJobRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request* Creates the definition for a model explainability job. *
* * @param createModelExplainabilityJobDefinitionRequest * @return Result of the CreateModelExplainabilityJobDefinition operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateModelExplainabilityJobDefinition * @see AWS API Documentation */ @Override public CreateModelExplainabilityJobDefinitionResult createModelExplainabilityJobDefinition(CreateModelExplainabilityJobDefinitionRequest request) { request = beforeClientExecution(request); return executeCreateModelExplainabilityJobDefinition(request); } @SdkInternalApi final CreateModelExplainabilityJobDefinitionResult executeCreateModelExplainabilityJobDefinition( CreateModelExplainabilityJobDefinitionRequest createModelExplainabilityJobDefinitionRequest) { ExecutionContext executionContext = createExecutionContext(createModelExplainabilityJobDefinitionRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, * or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web * Services Marketplace to create models in SageMaker. *
*
* To create a model package by specifying a Docker container that contains your inference code and the Amazon S3
* location of your model artifacts, provide values for InferenceSpecification. To create a model from
* an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value 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. *
*Experiment
* or Artifact.
* @throws ResourceLimitExceededException
* You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateModelPackage
* @see AWS
* API Documentation
*/
@Override
public CreateModelPackageResult createModelPackage(CreateModelPackageRequest request) {
request = beforeClientExecution(request);
return executeCreateModelPackage(request);
}
@SdkInternalApi
final CreateModelPackageResult executeCreateModelPackage(CreateModelPackageRequest createModelPackageRequest) {
ExecutionContext executionContext = createExecutionContext(createModelPackageRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request* Creates a model group. A model group contains a group of model versions. *
* * @param createModelPackageGroupRequest * @return Result of the CreateModelPackageGroup operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateModelPackageGroup * @see AWS API Documentation */ @Override public CreateModelPackageGroupResult createModelPackageGroup(CreateModelPackageGroupRequest request) { request = beforeClientExecution(request); return executeCreateModelPackageGroup(request); } @SdkInternalApi final CreateModelPackageGroupResult executeCreateModelPackageGroup(CreateModelPackageGroupRequest createModelPackageGroupRequest) { ExecutionContext executionContext = createExecutionContext(createModelPackageGroupRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* 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 Result of the CreateModelQualityJobDefinition operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateModelQualityJobDefinition * @see AWS API Documentation */ @Override public CreateModelQualityJobDefinitionResult createModelQualityJobDefinition(CreateModelQualityJobDefinitionRequest request) { request = beforeClientExecution(request); return executeCreateModelQualityJobDefinition(request); } @SdkInternalApi final CreateModelQualityJobDefinitionResult executeCreateModelQualityJobDefinition( CreateModelQualityJobDefinitionRequest createModelQualityJobDefinitionRequest) { ExecutionContext executionContext = createExecutionContext(createModelQualityJobDefinitionRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an * Amazon SageMaker Endpoint. *
* * @param createMonitoringScheduleRequest * @return Result of the CreateMonitoringSchedule operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateMonitoringSchedule * @see AWS API Documentation */ @Override public CreateMonitoringScheduleResult createMonitoringSchedule(CreateMonitoringScheduleRequest request) { request = beforeClientExecution(request); return executeCreateMonitoringSchedule(request); } @SdkInternalApi final CreateMonitoringScheduleResult executeCreateMonitoringSchedule(CreateMonitoringScheduleRequest createMonitoringScheduleRequest) { ExecutionContext executionContext = createExecutionContext(createMonitoringScheduleRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running * on a Jupyter notebook. *
*
* In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run.
* SageMaker launches the instance, installs common libraries that you can use to explore datasets for model
* training, and attaches an ML storage volume to the notebook instance.
*
* SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a * specific algorithm or with a machine learning framework. *
** After receiving the request, SageMaker does the following: *
** Creates a network interface in the SageMaker VPC. *
*
* (Option) If you specified SubnetId, SageMaker creates a network interface in your own VPC, which is
* inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker
* attaches the security group that you specified in the request to the network interface that it creates in your
* VPC.
*
* Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified
* SubnetId of your VPC, SageMaker specifies both network interfaces when launching this instance. This
* enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.
*
* After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN). You can't change the name * of a notebook instance after you create it. *
** After SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter * notebooks. For example, you can write code to explore a dataset that you can use for model training, train a * model, host models by creating SageMaker endpoints, and validate hosted models. *
** For more information, see How It * Works. *
* * @param createNotebookInstanceRequest * @return Result of the CreateNotebookInstance operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateNotebookInstance * @see AWS API Documentation */ @Override public CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest request) { request = beforeClientExecution(request); return executeCreateNotebookInstance(request); } @SdkInternalApi final CreateNotebookInstanceResult executeCreateNotebookInstance(CreateNotebookInstanceRequest createNotebookInstanceRequest) { ExecutionContext executionContext = createExecutionContext(createNotebookInstanceRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle * configuration is a collection of shell scripts that run when you create or start a notebook instance. *
** Each lifecycle configuration script has a limit of 16384 characters. *
*
* The value of the $PATH environment variable that is available to both scripts is
* /sbin:bin:/usr/sbin:/usr/bin.
*
* View CloudWatch Logs for notebook instance lifecycle configurations in log group
* /aws/sagemaker/NotebookInstances in log stream
* [notebook-instance-name]/[LifecycleConfigHook].
*
* Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, * it fails and the notebook instance is not created or started. *
** For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) * Customize a Notebook Instance. *
* * @param createNotebookInstanceLifecycleConfigRequest * @return Result of the CreateNotebookInstanceLifecycleConfig operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateNotebookInstanceLifecycleConfig * @see AWS API Documentation */ @Override public CreateNotebookInstanceLifecycleConfigResult createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest request) { request = beforeClientExecution(request); return executeCreateNotebookInstanceLifecycleConfig(request); } @SdkInternalApi final CreateNotebookInstanceLifecycleConfigResult executeCreateNotebookInstanceLifecycleConfig( CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest) { ExecutionContext executionContext = createExecutionContext(createNotebookInstanceLifecycleConfigRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a pipeline using a JSON pipeline definition. *
* * @param createPipelineRequest * @return Result of the CreatePipeline operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreatePipeline * @see AWS API * Documentation */ @Override public CreatePipelineResult createPipeline(CreatePipelineRequest request) { request = beforeClientExecution(request); return executeCreatePipeline(request); } @SdkInternalApi final CreatePipelineResult executeCreatePipeline(CreatePipelineRequest createPipelineRequest) { ExecutionContext executionContext = createExecutionContext(createPipelineRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be * automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated * with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the * authentication mode equals IAM. *
** The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is * created, no additional permission is required to access this URL. IAM authorization policies for this API are * also enforced for every HTTP request and WebSocket frame that attempts to connect to the app. *
** You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or * Amazon VPC Endpoints that you specify. For more information, see Connect to SageMaker Studio * Through an Interface VPC Endpoint . *
*
* The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You
* can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit
* expires, you are directed to the Amazon Web Services console sign-in page.
*
* Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker
* console, when you choose Open next to a notebook instance, SageMaker opens a new tab showing the
* Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.
*
* The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the * presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for * this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook * instance. *
*
* You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify.
* Use the NotIpAddress condition operator and the aws:SourceIP condition context key to
* specify the list of IP addresses that you want to have access to the notebook instance. For more information, see
* Limit Access to a Notebook Instance by IP Address.
*
* The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute * limit expires, you are directed to the Amazon Web Services console sign-in page. *
** Creates a processing job. *
* * @param createProcessingJobRequest * @return Result of the CreateProcessingJob operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.CreateProcessingJob * @see AWS * API Documentation */ @Override public CreateProcessingJobResult createProcessingJob(CreateProcessingJobRequest request) { request = beforeClientExecution(request); return executeCreateProcessingJob(request); } @SdkInternalApi final CreateProcessingJobResult executeCreateProcessingJob(CreateProcessingJobRequest createProcessingJobRequest) { ExecutionContext executionContext = createExecutionContext(createProcessingJobRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from * training to deploying an approved model. *
* * @param createProjectRequest * @return Result of the CreateProject operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateProject * @see AWS API * Documentation */ @Override public CreateProjectResult createProject(CreateProjectRequest request) { request = beforeClientExecution(request); return executeCreateProject(request); } @SdkInternalApi final CreateProjectResult executeCreateProject(CreateProjectRequest createProjectRequest) { ExecutionContext executionContext = createExecutionContext(createProjectRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a space used for real time collaboration in a Domain. *
* * @param createSpaceRequest * @return Result of the CreateSpace operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateSpace * @see AWS API * Documentation */ @Override public CreateSpaceResult createSpace(CreateSpaceRequest request) { request = beforeClientExecution(request); return executeCreateSpace(request); } @SdkInternalApi final CreateSpaceResult executeCreateSpace(CreateSpaceRequest createSpaceRequest) { ExecutionContext executionContext = createExecutionContext(createSpaceRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a new Studio Lifecycle Configuration. *
* * @param createStudioLifecycleConfigRequest * @return Result of the CreateStudioLifecycleConfig operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateStudioLifecycleConfig * @see AWS API Documentation */ @Override public CreateStudioLifecycleConfigResult createStudioLifecycleConfig(CreateStudioLifecycleConfigRequest request) { request = beforeClientExecution(request); return executeCreateStudioLifecycleConfig(request); } @SdkInternalApi final CreateStudioLifecycleConfigResult executeCreateStudioLifecycleConfig(CreateStudioLifecycleConfigRequest createStudioLifecycleConfigRequest) { ExecutionContext executionContext = createExecutionContext(createStudioLifecycleConfigRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon * S3 location that you specify. *
** If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as * part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided * that you know how to use them for inference. *
** In the request body, you provide the following: *
*
* AlgorithmSpecification - Identifies the training algorithm to use.
*
* HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model
* parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of
* hyperparameters for each training algorithm provided by SageMaker, see Algorithms.
*
* Do not include any security-sensitive information including account access IDs, secrets or tokens in any * hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your * training job request and return an exception error. *
*
* InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx
* location where it is stored.
*
* OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of
* model training.
*
* ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy
* for model training. In distributed training, you specify more than one instance.
*
* EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by
* using Amazon EC2 Spot instances. For more information, see Managed Spot
* Training.
*
* RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf
* during model training. You must grant this role the necessary permissions so that SageMaker can successfully
* complete model training.
*
* StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time
* limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to
* complete.
*
* Environment - The environment variables to set in the Docker container.
*
* RetryStrategy - The number of times to retry the job when the job fails due to an
* InternalServerError.
*
* For more information about SageMaker, see How It Works. *
* * @param createTrainingJobRequest * @return Result of the CreateTrainingJob operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.CreateTrainingJob * @see AWS * API Documentation */ @Override public CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest request) { request = beforeClientExecution(request); return executeCreateTrainingJob(request); } @SdkInternalApi final CreateTrainingJobResult executeCreateTrainingJob(CreateTrainingJobRequest createTrainingJobRequest) { ExecutionContext executionContext = createExecutionContext(createTrainingJobRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these * results to an Amazon S3 location that you specify. *
** To perform batch transformations, you create a transform job and use the data that you have readily available. *
** In the request body, you provide the following: *
*
* TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web
* Services Region in an Amazon Web Services account.
*
* ModelName - Identifies the model to use. ModelName must be the name of an existing
* Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on
* creating a model, see CreateModel.
*
* TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is
* stored.
*
* TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the
* results from the transform job.
*
* TransformResources - Identifies the ML compute instances for the transform job.
*
* For more information about how batch transformation works, see Batch Transform. *
* * @param createTransformJobRequest * @return Result of the CreateTransformJob operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.CreateTransformJob * @see AWS * API Documentation */ @Override public CreateTransformJobResult createTransformJob(CreateTransformJobRequest request) { request = beforeClientExecution(request); return executeCreateTransformJob(request); } @SdkInternalApi final CreateTransformJobResult executeCreateTransformJob(CreateTransformJobRequest createTransformJobRequest) { ExecutionContext executionContext = createExecutionContext(createTransformJobRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates an SageMaker trial. A trial is a set of steps called trial components that produce a * machine learning model. A trial is part of a single SageMaker experiment. *
** When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are * automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must * use the logging APIs provided by the SDK. *
** You can add tags to a trial and then use the Search API to search for the * tags. *
** To get a list of all your trials, call the ListTrials API. To view * a trial's properties, call the DescribeTrial API. To * create a trial component, call the CreateTrialComponent API. *
* * @param createTrialRequest * @return Result of the CreateTrial operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateTrial * @see AWS API * Documentation */ @Override public CreateTrialResult createTrial(CreateTrialRequest request) { request = beforeClientExecution(request); return executeCreateTrial(request); } @SdkInternalApi final CreateTrialResult executeCreateTrial(CreateTrialRequest createTrialRequest) { ExecutionContext executionContext = createExecutionContext(createTrialRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one * or more trial components. A trial component can be used in multiple trials. *
** Trial components include pre-processing jobs, training jobs, and batch transform jobs. *
** When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are * automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must * use the logging APIs provided by the SDK. *
** You can add tags to a trial component and then use the Search API to search for the * tags. *
* * @param createTrialComponentRequest * @return Result of the CreateTrialComponent operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateTrialComponent * @see AWS * API Documentation */ @Override public CreateTrialComponentResult createTrialComponent(CreateTrialComponentRequest request) { request = beforeClientExecution(request); return executeCreateTrialComponent(request); } @SdkInternalApi final CreateTrialComponentResult executeCreateTrialComponent(CreateTrialComponentRequest createTrialComponentRequest) { ExecutionContext executionContext = createExecutionContext(createTrialComponentRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* 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 * IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings * for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory. *
* * @param createUserProfileRequest * @return Result of the CreateUserProfile operation returned by the service. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.CreateUserProfile * @see AWS * API Documentation */ @Override public CreateUserProfileResult createUserProfile(CreateUserProfileRequest request) { request = beforeClientExecution(request); return executeCreateUserProfile(request); } @SdkInternalApi final CreateUserProfileResult executeCreateUserProfile(CreateUserProfileRequest createUserProfileRequest) { ExecutionContext executionContext = createExecutionContext(createUserProfileRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Use this operation to create a workforce. This operation will return an error if a workforce already exists in * the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services * Region per Amazon Web Services account. *
*
* If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the
* DeleteWorkforce
* API operation to delete the existing workforce and then use CreateWorkforce to create a new
* workforce.
*
* To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in
* CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console.
* For more information, see Create a Private
* Workforce (Amazon Cognito).
*
* To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in
* OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and
* Amazon A2I to create work teams. For more information, see Create a Private
* Workforce (OIDC IdP).
*
* 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 Result of the CreateWorkteam operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceLimitExceededException * You have exceeded an SageMaker resource limit. For example, you might have too many training jobs * created. * @sample AmazonSageMaker.CreateWorkteam * @see AWS API * Documentation */ @Override public CreateWorkteamResult createWorkteam(CreateWorkteamRequest request) { request = beforeClientExecution(request); return executeCreateWorkteam(request); } @SdkInternalApi final CreateWorkteamResult executeCreateWorkteam(CreateWorkteamRequest createWorkteamRequest) { ExecutionContext executionContext = createExecutionContext(createWorkteamRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Deletes an action. *
* * @param deleteActionRequest * @return Result of the DeleteAction operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteAction * @see AWS API * Documentation */ @Override public DeleteActionResult deleteAction(DeleteActionRequest request) { request = beforeClientExecution(request); return executeDeleteAction(request); } @SdkInternalApi final DeleteActionResult executeDeleteAction(DeleteActionRequest deleteActionRequest) { ExecutionContext executionContext = createExecutionContext(deleteActionRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Removes the specified algorithm from your account. *
* * @param deleteAlgorithmRequest * @return Result of the DeleteAlgorithm operation returned by the service. * @sample AmazonSageMaker.DeleteAlgorithm * @see AWS API * Documentation */ @Override public DeleteAlgorithmResult deleteAlgorithm(DeleteAlgorithmRequest request) { request = beforeClientExecution(request); return executeDeleteAlgorithm(request); } @SdkInternalApi final DeleteAlgorithmResult executeDeleteAlgorithm(DeleteAlgorithmRequest deleteAlgorithmRequest) { ExecutionContext executionContext = createExecutionContext(deleteAlgorithmRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Used to stop and delete an app. *
* * @param deleteAppRequest * @return Result of the DeleteApp operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteApp * @see AWS API * Documentation */ @Override public DeleteAppResult deleteApp(DeleteAppRequest request) { request = beforeClientExecution(request); return executeDeleteApp(request); } @SdkInternalApi final DeleteAppResult executeDeleteApp(DeleteAppRequest deleteAppRequest) { ExecutionContext executionContext = createExecutionContext(deleteAppRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Deletes an AppImageConfig. *
* * @param deleteAppImageConfigRequest * @return Result of the DeleteAppImageConfig operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteAppImageConfig * @see AWS * API Documentation */ @Override public DeleteAppImageConfigResult deleteAppImageConfig(DeleteAppImageConfigRequest request) { request = beforeClientExecution(request); return executeDeleteAppImageConfig(request); } @SdkInternalApi final DeleteAppImageConfigResult executeDeleteAppImageConfig(DeleteAppImageConfigRequest deleteAppImageConfigRequest) { ExecutionContext executionContext = createExecutionContext(deleteAppImageConfigRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request
* Deletes an artifact. Either ArtifactArn or Source must be specified.
*
* Deletes an association. *
* * @param deleteAssociationRequest * @return Result of the DeleteAssociation operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteAssociation * @see AWS * API Documentation */ @Override public DeleteAssociationResult deleteAssociation(DeleteAssociationRequest request) { request = beforeClientExecution(request); return executeDeleteAssociation(request); } @SdkInternalApi final DeleteAssociationResult executeDeleteAssociation(DeleteAssociationRequest deleteAssociationRequest) { ExecutionContext executionContext = createExecutionContext(deleteAssociationRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Deletes the specified Git repository from your account. *
* * @param deleteCodeRepositoryRequest * @return Result of the DeleteCodeRepository operation returned by the service. * @sample AmazonSageMaker.DeleteCodeRepository * @see AWS * API Documentation */ @Override public DeleteCodeRepositoryResult deleteCodeRepository(DeleteCodeRepositoryRequest request) { request = beforeClientExecution(request); return executeDeleteCodeRepository(request); } @SdkInternalApi final DeleteCodeRepositoryResult executeDeleteCodeRepository(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest) { ExecutionContext executionContext = createExecutionContext(deleteCodeRepositoryRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Deletes an context. *
* * @param deleteContextRequest * @return Result of the DeleteContext operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteContext * @see AWS API * Documentation */ @Override public DeleteContextResult deleteContext(DeleteContextRequest request) { request = beforeClientExecution(request); return executeDeleteContext(request); } @SdkInternalApi final DeleteContextResult executeDeleteContext(DeleteContextRequest deleteContextRequest) { ExecutionContext executionContext = createExecutionContext(deleteContextRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Deletes a data quality monitoring job definition. *
* * @param deleteDataQualityJobDefinitionRequest * @return Result of the DeleteDataQualityJobDefinition operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteDataQualityJobDefinition * @see AWS API Documentation */ @Override public DeleteDataQualityJobDefinitionResult deleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest request) { request = beforeClientExecution(request); return executeDeleteDataQualityJobDefinition(request); } @SdkInternalApi final DeleteDataQualityJobDefinitionResult executeDeleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest deleteDataQualityJobDefinitionRequest) { ExecutionContext executionContext = createExecutionContext(deleteDataQualityJobDefinitionRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Deletes a fleet. *
* * @param deleteDeviceFleetRequest * @return Result of the DeleteDeviceFleet operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.DeleteDeviceFleet * @see AWS * API Documentation */ @Override public DeleteDeviceFleetResult deleteDeviceFleet(DeleteDeviceFleetRequest request) { request = beforeClientExecution(request); return executeDeleteDeviceFleet(request); } @SdkInternalApi final DeleteDeviceFleetResult executeDeleteDeviceFleet(DeleteDeviceFleetRequest deleteDeviceFleetRequest) { ExecutionContext executionContext = createExecutionContext(deleteDeviceFleetRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again * using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS * volume, including data, notebooks, and other artifacts. *
* * @param deleteDomainRequest * @return Result of the DeleteDomain operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteDomain * @see AWS API * Documentation */ @Override public DeleteDomainResult deleteDomain(DeleteDomainRequest request) { request = beforeClientExecution(request); return executeDeleteDomain(request); } @SdkInternalApi final DeleteDomainResult executeDeleteDomain(DeleteDomainRequest deleteDomainRequest) { ExecutionContext executionContext = createExecutionContext(deleteDomainRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages * in the plan. *
* * @param deleteEdgeDeploymentPlanRequest * @return Result of the DeleteEdgeDeploymentPlan operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.DeleteEdgeDeploymentPlan * @see AWS API Documentation */ @Override public DeleteEdgeDeploymentPlanResult deleteEdgeDeploymentPlan(DeleteEdgeDeploymentPlanRequest request) { request = beforeClientExecution(request); return executeDeleteEdgeDeploymentPlan(request); } @SdkInternalApi final DeleteEdgeDeploymentPlanResult executeDeleteEdgeDeploymentPlan(DeleteEdgeDeploymentPlanRequest deleteEdgeDeploymentPlanRequest) { ExecutionContext executionContext = createExecutionContext(deleteEdgeDeploymentPlanRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Delete a stage in an edge deployment plan if (and only if) the stage is inactive. *
* * @param deleteEdgeDeploymentStageRequest * @return Result of the DeleteEdgeDeploymentStage operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.DeleteEdgeDeploymentStage * @see AWS API Documentation */ @Override public DeleteEdgeDeploymentStageResult deleteEdgeDeploymentStage(DeleteEdgeDeploymentStageRequest request) { request = beforeClientExecution(request); return executeDeleteEdgeDeploymentStage(request); } @SdkInternalApi final DeleteEdgeDeploymentStageResult executeDeleteEdgeDeploymentStage(DeleteEdgeDeploymentStageRequest deleteEdgeDeploymentStageRequest) { ExecutionContext executionContext = createExecutionContext(deleteEdgeDeploymentStageRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created. *
** SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call. *
*
* When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key
* grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do
* not delete or revoke the permissions for your
* ExecutionRoleArn
* , otherwise SageMaker cannot delete these resources.
*
* 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.
*
* Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a * list of the trials associated with the experiment. *
* * @param deleteExperimentRequest * @return Result of the DeleteExperiment operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteExperiment * @see AWS API * Documentation */ @Override public DeleteExperimentResult deleteExperiment(DeleteExperimentRequest request) { request = beforeClientExecution(request); return executeDeleteExperiment(request); } @SdkInternalApi final DeleteExperimentResult executeDeleteExperiment(DeleteExperimentRequest deleteExperimentRequest) { ExecutionContext executionContext = createExecutionContext(deleteExperimentRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request
* Delete the FeatureGroup and any data that was written to the OnlineStore of the
* FeatureGroup. Data cannot be accessed from the OnlineStore immediately after
* DeleteFeatureGroup is called.
*
* Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and
* tables that are automatically created for your OfflineStore are not deleted.
*
* Deletes the specified flow definition. *
* * @param deleteFlowDefinitionRequest * @return Result of the DeleteFlowDefinition operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteFlowDefinition * @see AWS * API Documentation */ @Override public DeleteFlowDefinitionResult deleteFlowDefinition(DeleteFlowDefinitionRequest request) { request = beforeClientExecution(request); return executeDeleteFlowDefinition(request); } @SdkInternalApi final DeleteFlowDefinitionResult executeDeleteFlowDefinition(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest) { ExecutionContext executionContext = createExecutionContext(deleteFlowDefinitionRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Delete a hub. *
** Hub APIs are only callable through SageMaker Studio. *
** Delete the contents of a hub. *
** Hub APIs are only callable through SageMaker Studio. *
** Use this operation to delete a human task user interface (worker task template). *
*
* To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis.
* When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.
*
* Deletes a SageMaker image and all versions of the image. The container images aren't deleted. *
* * @param deleteImageRequest * @return Result of the DeleteImage operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteImage * @see AWS API * Documentation */ @Override public DeleteImageResult deleteImage(DeleteImageRequest request) { request = beforeClientExecution(request); return executeDeleteImage(request); } @SdkInternalApi final DeleteImageResult executeDeleteImage(DeleteImageRequest deleteImageRequest) { ExecutionContext executionContext = createExecutionContext(deleteImageRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Deletes a version of a SageMaker image. The container image the version represents isn't deleted. *
* * @param deleteImageVersionRequest * @return Result of the DeleteImageVersion operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteImageVersion * @see AWS * API Documentation */ @Override public DeleteImageVersionResult deleteImageVersion(DeleteImageVersionRequest request) { request = beforeClientExecution(request); return executeDeleteImageVersion(request); } @SdkInternalApi final DeleteImageVersionResult executeDeleteImageVersion(DeleteImageVersionRequest deleteImageVersionRequest) { ExecutionContext executionContext = createExecutionContext(deleteImageVersionRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Deletes an inference experiment. *
** This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes * the metadata of your experiment. *
*Experiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteInferenceExperiment
* @see AWS API Documentation
*/
@Override
public DeleteInferenceExperimentResult deleteInferenceExperiment(DeleteInferenceExperimentRequest request) {
request = beforeClientExecution(request);
return executeDeleteInferenceExperiment(request);
}
@SdkInternalApi
final DeleteInferenceExperimentResult executeDeleteInferenceExperiment(DeleteInferenceExperimentRequest deleteInferenceExperimentRequest) {
ExecutionContext executionContext = createExecutionContext(deleteInferenceExperimentRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request
* Deletes a model. The DeleteModel API deletes only the model entry that was created in SageMaker when
* you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role
* that you specified when creating the model.
*
* Deletes an Amazon SageMaker model bias job definition. *
* * @param deleteModelBiasJobDefinitionRequest * @return Result of the DeleteModelBiasJobDefinition operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteModelBiasJobDefinition * @see AWS API Documentation */ @Override public DeleteModelBiasJobDefinitionResult deleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest request) { request = beforeClientExecution(request); return executeDeleteModelBiasJobDefinition(request); } @SdkInternalApi final DeleteModelBiasJobDefinitionResult executeDeleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest deleteModelBiasJobDefinitionRequest) { ExecutionContext executionContext = createExecutionContext(deleteModelBiasJobDefinitionRequest); AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics(); awsRequestMetrics.startEvent(Field.ClientExecuteTime); Request* Deletes an Amazon SageMaker Model Card. *
* * @param deleteModelCardRequest * @return Result of the DeleteModelCard operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @sample AmazonSageMaker.DeleteModelCard
* @see AWS API
* Documentation
*/
@Override
public DeleteModelCardResult deleteModelCard(DeleteModelCardRequest request) {
request = beforeClientExecution(request);
return executeDeleteModelCard(request);
}
@SdkInternalApi
final DeleteModelCardResult executeDeleteModelCard(DeleteModelCardRequest deleteModelCardRequest) {
ExecutionContext executionContext = createExecutionContext(deleteModelCardRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request