* Note: Do not directly implement this interface, new methods are added to it regularly. Extend from * {@link com.amazonaws.services.sagemaker.AbstractAmazonSageMaker} instead. *
**
* Provides APIs for creating and managing SageMaker resources. *
** Other Resources: *
** 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 */ AddAssociationResult addAssociation(AddAssociationRequest addAssociationRequest); /** ** 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 */ AssociateTrialComponentResult associateTrialComponent(AssociateTrialComponentRequest associateTrialComponentRequest); /** ** 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 */ BatchDescribeModelPackageResult batchDescribeModelPackage(BatchDescribeModelPackageRequest batchDescribeModelPackageRequest); /** ** 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 */ CreateActionResult createAction(CreateActionRequest createActionRequest); /** ** 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 */ CreateAlgorithmResult createAlgorithm(CreateAlgorithmRequest createAlgorithmRequest); /** ** 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 */ CreateAppResult createApp(CreateAppRequest createAppRequest); /** ** 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 */ CreateAppImageConfigResult createAppImageConfig(CreateAppImageConfigRequest createAppImageConfigRequest); /** ** 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 */ CreateArtifactResult createArtifact(CreateArtifactRequest createArtifactRequest); /** ** 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 */ CreateAutoMLJobResult createAutoMLJob(CreateAutoMLJobRequest createAutoMLJobRequest); /** ** 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 */ CreateAutoMLJobV2Result createAutoMLJobV2(CreateAutoMLJobV2Request createAutoMLJobV2Request); /** ** 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 */ CreateCodeRepositoryResult createCodeRepository(CreateCodeRepositoryRequest createCodeRepositoryRequest); /** ** 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 */ CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest createCompilationJobRequest); /** ** 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 */ CreateContextResult createContext(CreateContextRequest createContextRequest); /** ** 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 */ CreateDataQualityJobDefinitionResult createDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest createDataQualityJobDefinitionRequest); /** ** 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 */ CreateDeviceFleetResult createDeviceFleet(CreateDeviceFleetRequest createDeviceFleetRequest); /** *
* 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 */ CreateDomainResult createDomain(CreateDomainRequest createDomainRequest); /** ** 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 */ CreateEdgeDeploymentPlanResult createEdgeDeploymentPlan(CreateEdgeDeploymentPlanRequest createEdgeDeploymentPlanRequest); /** ** 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 */ CreateEdgeDeploymentStageResult createEdgeDeploymentStage(CreateEdgeDeploymentStageRequest createEdgeDeploymentStageRequest); /** ** 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 */ CreateEdgePackagingJobResult createEdgePackagingJob(CreateEdgePackagingJobRequest createEdgePackagingJobRequest); /** ** 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 */ CreateExperimentResult createExperiment(CreateExperimentRequest createExperimentRequest); /** *
* 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 */ CreateFlowDefinitionResult createFlowDefinition(CreateFlowDefinitionRequest createFlowDefinitionRequest); /** ** 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 */ CreateHumanTaskUiResult createHumanTaskUi(CreateHumanTaskUiRequest createHumanTaskUiRequest); /** ** 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 */ CreateImageResult createImage(CreateImageRequest createImageRequest); /** *
* 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 */ CreateInferenceExperimentResult createInferenceExperiment(CreateInferenceExperimentRequest createInferenceExperimentRequest); /** ** 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 */ CreateInferenceRecommendationsJobResult createInferenceRecommendationsJob(CreateInferenceRecommendationsJobRequest createInferenceRecommendationsJobRequest); /** ** 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 */ CreateModelResult createModel(CreateModelRequest createModelRequest); /** ** 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 */ CreateModelBiasJobDefinitionResult createModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest createModelBiasJobDefinitionRequest); /** ** 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
*/
CreateModelCardResult createModelCard(CreateModelCardRequest createModelCardRequest);
/**
* * 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
*/
CreateModelCardExportJobResult createModelCardExportJob(CreateModelCardExportJobRequest createModelCardExportJobRequest);
/**
* * 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 */ CreateModelExplainabilityJobDefinitionResult createModelExplainabilityJobDefinition( CreateModelExplainabilityJobDefinitionRequest createModelExplainabilityJobDefinitionRequest); /** ** 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
*/
CreateModelPackageResult createModelPackage(CreateModelPackageRequest createModelPackageRequest);
/**
* * 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 */ CreateModelPackageGroupResult createModelPackageGroup(CreateModelPackageGroupRequest createModelPackageGroupRequest); /** ** 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 */ CreateModelQualityJobDefinitionResult createModelQualityJobDefinition(CreateModelQualityJobDefinitionRequest createModelQualityJobDefinitionRequest); /** ** 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 */ CreateMonitoringScheduleResult createMonitoringSchedule(CreateMonitoringScheduleRequest createMonitoringScheduleRequest); /** ** 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 */ CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest createNotebookInstanceRequest); /** ** 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 */ CreateNotebookInstanceLifecycleConfigResult createNotebookInstanceLifecycleConfig( CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest); /** ** 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 */ CreatePipelineResult createPipeline(CreatePipelineRequest createPipelineRequest); /** ** 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 */ CreateProcessingJobResult createProcessingJob(CreateProcessingJobRequest createProcessingJobRequest); /** ** 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 */ CreateProjectResult createProject(CreateProjectRequest createProjectRequest); /** ** 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 */ CreateSpaceResult createSpace(CreateSpaceRequest createSpaceRequest); /** ** 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 */ CreateStudioLifecycleConfigResult createStudioLifecycleConfig(CreateStudioLifecycleConfigRequest createStudioLifecycleConfigRequest); /** ** 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 */ CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest createTrainingJobRequest); /** ** 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 */ CreateTransformJobResult createTransformJob(CreateTransformJobRequest createTransformJobRequest); /** ** 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 */ CreateTrialResult createTrial(CreateTrialRequest createTrialRequest); /** ** 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 */ CreateTrialComponentResult createTrialComponent(CreateTrialComponentRequest createTrialComponentRequest); /** ** 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 */ CreateUserProfileResult createUserProfile(CreateUserProfileRequest createUserProfileRequest); /** ** 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 */ CreateWorkteamResult createWorkteam(CreateWorkteamRequest createWorkteamRequest); /** ** 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 */ DeleteActionResult deleteAction(DeleteActionRequest deleteActionRequest); /** ** 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 */ DeleteAlgorithmResult deleteAlgorithm(DeleteAlgorithmRequest deleteAlgorithmRequest); /** ** 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 */ DeleteAppResult deleteApp(DeleteAppRequest deleteAppRequest); /** ** 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 */ DeleteAppImageConfigResult deleteAppImageConfig(DeleteAppImageConfigRequest deleteAppImageConfigRequest); /** *
* 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 */ DeleteAssociationResult deleteAssociation(DeleteAssociationRequest deleteAssociationRequest); /** ** 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 */ DeleteCodeRepositoryResult deleteCodeRepository(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest); /** ** 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 */ DeleteContextResult deleteContext(DeleteContextRequest deleteContextRequest); /** ** 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 */ DeleteDataQualityJobDefinitionResult deleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest deleteDataQualityJobDefinitionRequest); /** ** 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 */ DeleteDeviceFleetResult deleteDeviceFleet(DeleteDeviceFleetRequest deleteDeviceFleetRequest); /** ** 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 */ DeleteDomainResult deleteDomain(DeleteDomainRequest deleteDomainRequest); /** ** 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 */ DeleteEdgeDeploymentPlanResult deleteEdgeDeploymentPlan(DeleteEdgeDeploymentPlanRequest deleteEdgeDeploymentPlanRequest); /** ** 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 */ DeleteEdgeDeploymentStageResult deleteEdgeDeploymentStage(DeleteEdgeDeploymentStageRequest deleteEdgeDeploymentStageRequest); /** ** 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 */ DeleteExperimentResult deleteExperiment(DeleteExperimentRequest deleteExperimentRequest); /** *
* 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 */ DeleteFlowDefinitionResult deleteFlowDefinition(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest); /** ** 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 */ DeleteImageResult deleteImage(DeleteImageRequest deleteImageRequest); /** ** 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 */ DeleteImageVersionResult deleteImageVersion(DeleteImageVersionRequest deleteImageVersionRequest); /** ** 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
*/
DeleteInferenceExperimentResult deleteInferenceExperiment(DeleteInferenceExperimentRequest deleteInferenceExperimentRequest);
/**
*
* 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 */ DeleteModelBiasJobDefinitionResult deleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest deleteModelBiasJobDefinitionRequest); /** ** 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
*/
DeleteModelCardResult deleteModelCard(DeleteModelCardRequest deleteModelCardRequest);
/**
* * Deletes an Amazon SageMaker model explainability job definition. *
* * @param deleteModelExplainabilityJobDefinitionRequest * @return Result of the DeleteModelExplainabilityJobDefinition operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteModelExplainabilityJobDefinition * @see AWS API Documentation */ DeleteModelExplainabilityJobDefinitionResult deleteModelExplainabilityJobDefinition( DeleteModelExplainabilityJobDefinitionRequest deleteModelExplainabilityJobDefinitionRequest); /** ** Deletes a model package. *
** A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can * subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker. *
* * @param deleteModelPackageRequest * @return Result of the DeleteModelPackage operation returned by the service. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @sample AmazonSageMaker.DeleteModelPackage
* @see AWS
* API Documentation
*/
DeleteModelPackageResult deleteModelPackage(DeleteModelPackageRequest deleteModelPackageRequest);
/**
* * Deletes the specified model group. *
* * @param deleteModelPackageGroupRequest * @return Result of the DeleteModelPackageGroup operation returned by the service. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @sample AmazonSageMaker.DeleteModelPackageGroup
* @see AWS API Documentation
*/
DeleteModelPackageGroupResult deleteModelPackageGroup(DeleteModelPackageGroupRequest deleteModelPackageGroupRequest);
/**
* * Deletes a model group resource policy. *
* * @param deleteModelPackageGroupPolicyRequest * @return Result of the DeleteModelPackageGroupPolicy operation returned by the service. * @sample AmazonSageMaker.DeleteModelPackageGroupPolicy * @see AWS API Documentation */ DeleteModelPackageGroupPolicyResult deleteModelPackageGroupPolicy(DeleteModelPackageGroupPolicyRequest deleteModelPackageGroupPolicyRequest); /** ** Deletes the secified model quality monitoring job definition. *
* * @param deleteModelQualityJobDefinitionRequest * @return Result of the DeleteModelQualityJobDefinition operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteModelQualityJobDefinition * @see AWS API Documentation */ DeleteModelQualityJobDefinitionResult deleteModelQualityJobDefinition(DeleteModelQualityJobDefinitionRequest deleteModelQualityJobDefinitionRequest); /** ** Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job * execution history of the monitoring schedule. *
* * @param deleteMonitoringScheduleRequest * @return Result of the DeleteMonitoringSchedule operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteMonitoringSchedule * @see AWS API Documentation */ DeleteMonitoringScheduleResult deleteMonitoringSchedule(DeleteMonitoringScheduleRequest deleteMonitoringScheduleRequest); /** *
* Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you must call the
* StopNotebookInstance API.
*
* When you delete a notebook instance, you lose all of your data. SageMaker removes the ML compute instance, and * deletes the ML storage volume and the network interface associated with the notebook instance. *
** Deletes a notebook instance lifecycle configuration. *
* * @param deleteNotebookInstanceLifecycleConfigRequest * @return Result of the DeleteNotebookInstanceLifecycleConfig operation returned by the service. * @sample AmazonSageMaker.DeleteNotebookInstanceLifecycleConfig * @see AWS API Documentation */ DeleteNotebookInstanceLifecycleConfigResult deleteNotebookInstanceLifecycleConfig( DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest); /** *
* Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all
* running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline,
* all instances of the pipeline are deleted.
*
* Delete the specified project. *
* * @param deleteProjectRequest * @return Result of the DeleteProject operation returned by the service. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @sample AmazonSageMaker.DeleteProject
* @see AWS API
* Documentation
*/
DeleteProjectResult deleteProject(DeleteProjectRequest deleteProjectRequest);
/**
* * Used to delete a space. *
* * @param deleteSpaceRequest * @return Result of the DeleteSpace operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteSpace * @see AWS API * Documentation */ DeleteSpaceResult deleteSpace(DeleteSpaceRequest deleteSpaceRequest); /** ** Deletes the Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no * running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from * UserSettings in all Domains and UserProfiles. *
* * @param deleteStudioLifecycleConfigRequest * @return Result of the DeleteStudioLifecycleConfig operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.DeleteStudioLifecycleConfig * @see AWS API Documentation */ DeleteStudioLifecycleConfigResult deleteStudioLifecycleConfig(DeleteStudioLifecycleConfigRequest deleteStudioLifecycleConfigRequest); /** ** Deletes the specified tags from an SageMaker resource. *
*
* To list a resource's tags, use the ListTags API.
*
* When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from * training jobs that the hyperparameter tuning job launched before you called this API. *
** When you call this API to delete tags from a SageMaker Studio Domain or User Profile, the deleted tags are not * removed from Apps that the SageMaker Studio Domain or User Profile launched before you called this API. *
** Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components. *
* * @param deleteTrialRequest * @return Result of the DeleteTrial operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteTrial * @see AWS API * Documentation */ DeleteTrialResult deleteTrial(DeleteTrialRequest deleteTrialRequest); /** ** Deletes the specified trial component. A trial component must be disassociated from all trials before the trial * component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API. *
* * @param deleteTrialComponentRequest * @return Result of the DeleteTrialComponent operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteTrialComponent * @see AWS * API Documentation */ DeleteTrialComponentResult deleteTrialComponent(DeleteTrialComponentRequest deleteTrialComponentRequest); /** ** Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including * data, notebooks, and other artifacts. *
* * @param deleteUserProfileRequest * @return Result of the DeleteUserProfile operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DeleteUserProfile * @see AWS * API Documentation */ DeleteUserProfileResult deleteUserProfile(DeleteUserProfileRequest deleteUserProfileRequest); /** ** Use this operation to delete a workforce. *
** If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this * operation to delete the existing workforce and then use CreateWorkforce to * create a new workforce. *
*
* If a private workforce contains one or more work teams, you must use the DeleteWorkteam
* operation to delete all work teams before you delete the workforce. If you try to delete a workforce that
* contains one or more work teams, you will recieve a ResourceInUse error.
*
* Deletes an existing work team. This operation can't be undone. *
* * @param deleteWorkteamRequest * @return Result of the DeleteWorkteam 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.DeleteWorkteam * @see AWS API * Documentation */ DeleteWorkteamResult deleteWorkteam(DeleteWorkteamRequest deleteWorkteamRequest); /** ** Deregisters the specified devices. After you deregister a device, you will need to re-register the devices. *
* * @param deregisterDevicesRequest * @return Result of the DeregisterDevices operation returned by the service. * @sample AmazonSageMaker.DeregisterDevices * @see AWS * API Documentation */ DeregisterDevicesResult deregisterDevices(DeregisterDevicesRequest deregisterDevicesRequest); /** ** Describes an action. *
* * @param describeActionRequest * @return Result of the DescribeAction operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeAction * @see AWS API * Documentation */ DescribeActionResult describeAction(DescribeActionRequest describeActionRequest); /** ** Returns a description of the specified algorithm that is in your account. *
* * @param describeAlgorithmRequest * @return Result of the DescribeAlgorithm operation returned by the service. * @sample AmazonSageMaker.DescribeAlgorithm * @see AWS * API Documentation */ DescribeAlgorithmResult describeAlgorithm(DescribeAlgorithmRequest describeAlgorithmRequest); /** ** Describes the app. *
* * @param describeAppRequest * @return Result of the DescribeApp operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeApp * @see AWS API * Documentation */ DescribeAppResult describeApp(DescribeAppRequest describeAppRequest); /** ** Describes an AppImageConfig. *
* * @param describeAppImageConfigRequest * @return Result of the DescribeAppImageConfig operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeAppImageConfig * @see AWS API Documentation */ DescribeAppImageConfigResult describeAppImageConfig(DescribeAppImageConfigRequest describeAppImageConfigRequest); /** ** Describes an artifact. *
* * @param describeArtifactRequest * @return Result of the DescribeArtifact operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeArtifact * @see AWS API * Documentation */ DescribeArtifactResult describeArtifact(DescribeArtifactRequest describeArtifactRequest); /** ** Returns information about an AutoML job created by calling CreateAutoMLJob. *
*
* AutoML jobs created by calling CreateAutoMLJobV2
* cannot be described by DescribeAutoMLJob.
*
* Returns information about an AutoML job created by calling CreateAutoMLJobV2 * or CreateAutoMLJob. *
* * @param describeAutoMLJobV2Request * @return Result of the DescribeAutoMLJobV2 operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeAutoMLJobV2 * @see AWS * API Documentation */ DescribeAutoMLJobV2Result describeAutoMLJobV2(DescribeAutoMLJobV2Request describeAutoMLJobV2Request); /** ** Gets details about the specified Git repository. *
* * @param describeCodeRepositoryRequest * @return Result of the DescribeCodeRepository operation returned by the service. * @sample AmazonSageMaker.DescribeCodeRepository * @see AWS API Documentation */ DescribeCodeRepositoryResult describeCodeRepository(DescribeCodeRepositoryRequest describeCodeRepositoryRequest); /** ** Returns information about a model compilation job. *
** To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs. *
* * @param describeCompilationJobRequest * @return Result of the DescribeCompilationJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeCompilationJob * @see AWS API Documentation */ DescribeCompilationJobResult describeCompilationJob(DescribeCompilationJobRequest describeCompilationJobRequest); /** ** Describes a context. *
* * @param describeContextRequest * @return Result of the DescribeContext operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeContext * @see AWS API * Documentation */ DescribeContextResult describeContext(DescribeContextRequest describeContextRequest); /** ** Gets the details of a data quality monitoring job definition. *
* * @param describeDataQualityJobDefinitionRequest * @return Result of the DescribeDataQualityJobDefinition operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeDataQualityJobDefinition * @see AWS API Documentation */ DescribeDataQualityJobDefinitionResult describeDataQualityJobDefinition(DescribeDataQualityJobDefinitionRequest describeDataQualityJobDefinitionRequest); /** ** Describes the device. *
* * @param describeDeviceRequest * @return Result of the DescribeDevice operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeDevice * @see AWS API * Documentation */ DescribeDeviceResult describeDevice(DescribeDeviceRequest describeDeviceRequest); /** ** A description of the fleet the device belongs to. *
* * @param describeDeviceFleetRequest * @return Result of the DescribeDeviceFleet operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeDeviceFleet * @see AWS * API Documentation */ DescribeDeviceFleetResult describeDeviceFleet(DescribeDeviceFleetRequest describeDeviceFleetRequest); /** ** The description of the domain. *
* * @param describeDomainRequest * @return Result of the DescribeDomain operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeDomain * @see AWS API * Documentation */ DescribeDomainResult describeDomain(DescribeDomainRequest describeDomainRequest); /** ** Describes an edge deployment plan with deployment status per stage. *
* * @param describeEdgeDeploymentPlanRequest * @return Result of the DescribeEdgeDeploymentPlan operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeEdgeDeploymentPlan * @see AWS API Documentation */ DescribeEdgeDeploymentPlanResult describeEdgeDeploymentPlan(DescribeEdgeDeploymentPlanRequest describeEdgeDeploymentPlanRequest); /** ** A description of edge packaging jobs. *
* * @param describeEdgePackagingJobRequest * @return Result of the DescribeEdgePackagingJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeEdgePackagingJob * @see AWS API Documentation */ DescribeEdgePackagingJobResult describeEdgePackagingJob(DescribeEdgePackagingJobRequest describeEdgePackagingJobRequest); /** ** Returns the description of an endpoint. *
* * @param describeEndpointRequest * @return Result of the DescribeEndpoint operation returned by the service. * @sample AmazonSageMaker.DescribeEndpoint * @see AWS API * Documentation */ DescribeEndpointResult describeEndpoint(DescribeEndpointRequest describeEndpointRequest); /** *
* Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
*
* Provides a list of an experiment's properties. *
* * @param describeExperimentRequest * @return Result of the DescribeExperiment operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeExperiment * @see AWS * API Documentation */ DescribeExperimentResult describeExperiment(DescribeExperimentRequest describeExperimentRequest); /** *
* Use this operation to describe a FeatureGroup. The response includes information on the creation
* time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.
*
* Shows the metadata for a feature within a feature group. *
* * @param describeFeatureMetadataRequest * @return Result of the DescribeFeatureMetadata operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeFeatureMetadata * @see AWS API Documentation */ DescribeFeatureMetadataResult describeFeatureMetadata(DescribeFeatureMetadataRequest describeFeatureMetadataRequest); /** ** Returns information about the specified flow definition. *
* * @param describeFlowDefinitionRequest * @return Result of the DescribeFlowDefinition operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeFlowDefinition * @see AWS API Documentation */ DescribeFlowDefinitionResult describeFlowDefinition(DescribeFlowDefinitionRequest describeFlowDefinitionRequest); /** ** Describe a hub. *
** Hub APIs are only callable through SageMaker Studio. *
** Describe the content of a hub. *
** Hub APIs are only callable through SageMaker Studio. *
** Returns information about the requested human task user interface (worker task template). *
* * @param describeHumanTaskUiRequest * @return Result of the DescribeHumanTaskUi operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeHumanTaskUi * @see AWS * API Documentation */ DescribeHumanTaskUiResult describeHumanTaskUi(DescribeHumanTaskUiRequest describeHumanTaskUiRequest); /** ** Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include * the name, Amazon Resource Name (ARN), job status of your tuning job and more. *
* * @param describeHyperParameterTuningJobRequest * @return Result of the DescribeHyperParameterTuningJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeHyperParameterTuningJob * @see AWS API Documentation */ DescribeHyperParameterTuningJobResult describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest describeHyperParameterTuningJobRequest); /** ** Describes a SageMaker image. *
* * @param describeImageRequest * @return Result of the DescribeImage operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeImage * @see AWS API * Documentation */ DescribeImageResult describeImage(DescribeImageRequest describeImageRequest); /** ** Describes a version of a SageMaker image. *
* * @param describeImageVersionRequest * @return Result of the DescribeImageVersion operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeImageVersion * @see AWS * API Documentation */ DescribeImageVersionResult describeImageVersion(DescribeImageVersionRequest describeImageVersionRequest); /** ** Returns details about an inference experiment. *
* * @param describeInferenceExperimentRequest * @return Result of the DescribeInferenceExperiment operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeInferenceExperiment * @see AWS API Documentation */ DescribeInferenceExperimentResult describeInferenceExperiment(DescribeInferenceExperimentRequest describeInferenceExperimentRequest); /** ** Provides the results of the Inference Recommender job. One or more recommendation jobs are returned. *
* * @param describeInferenceRecommendationsJobRequest * @return Result of the DescribeInferenceRecommendationsJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeInferenceRecommendationsJob * @see AWS API Documentation */ DescribeInferenceRecommendationsJobResult describeInferenceRecommendationsJob( DescribeInferenceRecommendationsJobRequest describeInferenceRecommendationsJobRequest); /** ** Gets information about a labeling job. *
* * @param describeLabelingJobRequest * @return Result of the DescribeLabelingJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeLabelingJob * @see AWS * API Documentation */ DescribeLabelingJobResult describeLabelingJob(DescribeLabelingJobRequest describeLabelingJobRequest); /** ** Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage * Tracking in the Amazon SageMaker Developer Guide. *
* * @param describeLineageGroupRequest * @return Result of the DescribeLineageGroup operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeLineageGroup * @see AWS * API Documentation */ DescribeLineageGroupResult describeLineageGroup(DescribeLineageGroupRequest describeLineageGroupRequest); /** *
* Describes a model that you created using the CreateModel API.
*
* Returns a description of a model bias job definition. *
* * @param describeModelBiasJobDefinitionRequest * @return Result of the DescribeModelBiasJobDefinition operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeModelBiasJobDefinition * @see AWS API Documentation */ DescribeModelBiasJobDefinitionResult describeModelBiasJobDefinition(DescribeModelBiasJobDefinitionRequest describeModelBiasJobDefinitionRequest); /** ** Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card. *
* * @param describeModelCardRequest * @return Result of the DescribeModelCard operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeModelCard * @see AWS * API Documentation */ DescribeModelCardResult describeModelCard(DescribeModelCardRequest describeModelCardRequest); /** ** Describes an Amazon SageMaker Model Card export job. *
* * @param describeModelCardExportJobRequest * @return Result of the DescribeModelCardExportJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeModelCardExportJob * @see AWS API Documentation */ DescribeModelCardExportJobResult describeModelCardExportJob(DescribeModelCardExportJobRequest describeModelCardExportJobRequest); /** ** Returns a description of a model explainability job definition. *
* * @param describeModelExplainabilityJobDefinitionRequest * @return Result of the DescribeModelExplainabilityJobDefinition operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeModelExplainabilityJobDefinition * @see AWS API Documentation */ DescribeModelExplainabilityJobDefinitionResult describeModelExplainabilityJobDefinition( DescribeModelExplainabilityJobDefinitionRequest describeModelExplainabilityJobDefinitionRequest); /** ** Returns a description of the specified model package, which is used to create SageMaker models or list them on * Amazon Web Services Marketplace. *
** To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace. *
* * @param describeModelPackageRequest * @return Result of the DescribeModelPackage operation returned by the service. * @sample AmazonSageMaker.DescribeModelPackage * @see AWS * API Documentation */ DescribeModelPackageResult describeModelPackage(DescribeModelPackageRequest describeModelPackageRequest); /** ** Gets a description for the specified model group. *
* * @param describeModelPackageGroupRequest * @return Result of the DescribeModelPackageGroup operation returned by the service. * @sample AmazonSageMaker.DescribeModelPackageGroup * @see AWS API Documentation */ DescribeModelPackageGroupResult describeModelPackageGroup(DescribeModelPackageGroupRequest describeModelPackageGroupRequest); /** ** Returns a description of a model quality job definition. *
* * @param describeModelQualityJobDefinitionRequest * @return Result of the DescribeModelQualityJobDefinition operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeModelQualityJobDefinition * @see AWS API Documentation */ DescribeModelQualityJobDefinitionResult describeModelQualityJobDefinition(DescribeModelQualityJobDefinitionRequest describeModelQualityJobDefinitionRequest); /** ** Describes the schedule for a monitoring job. *
* * @param describeMonitoringScheduleRequest * @return Result of the DescribeMonitoringSchedule operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeMonitoringSchedule * @see AWS API Documentation */ DescribeMonitoringScheduleResult describeMonitoringSchedule(DescribeMonitoringScheduleRequest describeMonitoringScheduleRequest); /** ** Returns information about a notebook instance. *
* * @param describeNotebookInstanceRequest * @return Result of the DescribeNotebookInstance operation returned by the service. * @sample AmazonSageMaker.DescribeNotebookInstance * @see AWS API Documentation */ DescribeNotebookInstanceResult describeNotebookInstance(DescribeNotebookInstanceRequest describeNotebookInstanceRequest); /** ** Returns a description of a notebook instance lifecycle configuration. *
** For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) * Customize a Notebook Instance. *
* * @param describeNotebookInstanceLifecycleConfigRequest * @return Result of the DescribeNotebookInstanceLifecycleConfig operation returned by the service. * @sample AmazonSageMaker.DescribeNotebookInstanceLifecycleConfig * @see AWS API Documentation */ DescribeNotebookInstanceLifecycleConfigResult describeNotebookInstanceLifecycleConfig( DescribeNotebookInstanceLifecycleConfigRequest describeNotebookInstanceLifecycleConfigRequest); /** ** Describes the details of a pipeline. *
* * @param describePipelineRequest * @return Result of the DescribePipeline operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribePipeline * @see AWS API * Documentation */ DescribePipelineResult describePipeline(DescribePipelineRequest describePipelineRequest); /** ** Describes the details of an execution's pipeline definition. *
* * @param describePipelineDefinitionForExecutionRequest * @return Result of the DescribePipelineDefinitionForExecution operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribePipelineDefinitionForExecution * @see AWS API Documentation */ DescribePipelineDefinitionForExecutionResult describePipelineDefinitionForExecution( DescribePipelineDefinitionForExecutionRequest describePipelineDefinitionForExecutionRequest); /** ** Describes the details of a pipeline execution. *
* * @param describePipelineExecutionRequest * @return Result of the DescribePipelineExecution operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribePipelineExecution * @see AWS API Documentation */ DescribePipelineExecutionResult describePipelineExecution(DescribePipelineExecutionRequest describePipelineExecutionRequest); /** ** Returns a description of a processing job. *
* * @param describeProcessingJobRequest * @return Result of the DescribeProcessingJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeProcessingJob * @see AWS API Documentation */ DescribeProcessingJobResult describeProcessingJob(DescribeProcessingJobRequest describeProcessingJobRequest); /** ** Describes the details of a project. *
* * @param describeProjectRequest * @return Result of the DescribeProject operation returned by the service. * @sample AmazonSageMaker.DescribeProject * @see AWS API * Documentation */ DescribeProjectResult describeProject(DescribeProjectRequest describeProjectRequest); /** ** Describes the space. *
* * @param describeSpaceRequest * @return Result of the DescribeSpace operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeSpace * @see AWS API * Documentation */ DescribeSpaceResult describeSpace(DescribeSpaceRequest describeSpaceRequest); /** ** Describes the Studio Lifecycle Configuration. *
* * @param describeStudioLifecycleConfigRequest * @return Result of the DescribeStudioLifecycleConfig operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeStudioLifecycleConfig * @see AWS API Documentation */ DescribeStudioLifecycleConfigResult describeStudioLifecycleConfig(DescribeStudioLifecycleConfigRequest describeStudioLifecycleConfigRequest); /** ** Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor * in the Amazon Web Services Marketplace. *
* * @param describeSubscribedWorkteamRequest * @return Result of the DescribeSubscribedWorkteam operation returned by the service. * @sample AmazonSageMaker.DescribeSubscribedWorkteam * @see AWS API Documentation */ DescribeSubscribedWorkteamResult describeSubscribedWorkteam(DescribeSubscribedWorkteamRequest describeSubscribedWorkteamRequest); /** ** Returns information about a training job. *
*
* Some of the attributes below only appear if the training job successfully starts. If the training job fails,
* TrainingJobStatus is Failed and, depending on the FailureReason,
* attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime,
* and BillableTimeInSeconds may not be present in the response.
*
* Returns information about a transform job. *
* * @param describeTransformJobRequest * @return Result of the DescribeTransformJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeTransformJob * @see AWS * API Documentation */ DescribeTransformJobResult describeTransformJob(DescribeTransformJobRequest describeTransformJobRequest); /** ** Provides a list of a trial's properties. *
* * @param describeTrialRequest * @return Result of the DescribeTrial operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeTrial * @see AWS API * Documentation */ DescribeTrialResult describeTrial(DescribeTrialRequest describeTrialRequest); /** ** Provides a list of a trials component's properties. *
* * @param describeTrialComponentRequest * @return Result of the DescribeTrialComponent operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.DescribeTrialComponent * @see AWS API Documentation */ DescribeTrialComponentResult describeTrialComponent(DescribeTrialComponentRequest describeTrialComponentRequest); /** *
* Describes a user profile. For more information, see CreateUserProfile.
*
* Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, * allowed IP address ranges (CIDRs). Allowable IP address ranges * are the IP addresses that workers can use to access tasks. *
** This operation applies only to private workforces. *
** Gets information about a specific work team. You can see information such as the create date, the last updated * date, membership information, and the work team's Amazon Resource Name (ARN). *
* * @param describeWorkteamRequest * @return Result of the DescribeWorkteam operation returned by the service. * @sample AmazonSageMaker.DescribeWorkteam * @see AWS API * Documentation */ DescribeWorkteamResult describeWorkteam(DescribeWorkteamRequest describeWorkteamRequest); /** ** Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects. *
* * @param disableSagemakerServicecatalogPortfolioRequest * @return Result of the DisableSagemakerServicecatalogPortfolio operation returned by the service. * @sample AmazonSageMaker.DisableSagemakerServicecatalogPortfolio * @see AWS API Documentation */ DisableSagemakerServicecatalogPortfolioResult disableSagemakerServicecatalogPortfolio( DisableSagemakerServicecatalogPortfolioRequest disableSagemakerServicecatalogPortfolioRequest); /** ** Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. * Before you can delete a component, you must disassociate the component from all trials it is associated with. To * associate a trial component with a trial, call the AssociateTrialComponent API. *
*
* To get a list of the trials a component is associated with, use the Search API. Specify
* ExperimentTrialComponent for the Resource parameter. The list appears in the response
* under Results.TrialComponent.Parents.
*
* Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects. *
* * @param enableSagemakerServicecatalogPortfolioRequest * @return Result of the EnableSagemakerServicecatalogPortfolio operation returned by the service. * @sample AmazonSageMaker.EnableSagemakerServicecatalogPortfolio * @see AWS API Documentation */ EnableSagemakerServicecatalogPortfolioResult enableSagemakerServicecatalogPortfolio( EnableSagemakerServicecatalogPortfolioRequest enableSagemakerServicecatalogPortfolioRequest); /** ** Describes a fleet. *
* * @param getDeviceFleetReportRequest * @return Result of the GetDeviceFleetReport operation returned by the service. * @sample AmazonSageMaker.GetDeviceFleetReport * @see AWS * API Documentation */ GetDeviceFleetReportResult getDeviceFleetReport(GetDeviceFleetReportRequest getDeviceFleetReportRequest); /** ** The resource policy for the lineage group. *
* * @param getLineageGroupPolicyRequest * @return Result of the GetLineageGroupPolicy operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.GetLineageGroupPolicy * @see AWS API Documentation */ GetLineageGroupPolicyResult getLineageGroupPolicy(GetLineageGroupPolicyRequest getLineageGroupPolicyRequest); /** ** Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based * policies and resource-based policies in the Amazon Web Services Identity and Access Management User * Guide.. *
* * @param getModelPackageGroupPolicyRequest * @return Result of the GetModelPackageGroupPolicy operation returned by the service. * @sample AmazonSageMaker.GetModelPackageGroupPolicy * @see AWS API Documentation */ GetModelPackageGroupPolicyResult getModelPackageGroupPolicy(GetModelPackageGroupPolicyRequest getModelPackageGroupPolicyRequest); /** ** Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects. *
* * @param getSagemakerServicecatalogPortfolioStatusRequest * @return Result of the GetSagemakerServicecatalogPortfolioStatus operation returned by the service. * @sample AmazonSageMaker.GetSagemakerServicecatalogPortfolioStatus * @see AWS API Documentation */ GetSagemakerServicecatalogPortfolioStatusResult getSagemakerServicecatalogPortfolioStatus( GetSagemakerServicecatalogPortfolioStatusRequest getSagemakerServicecatalogPortfolioStatusRequest); /** *
* An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible
* matches for the property name to use in Search queries. Provides suggestions for
* HyperParameters, Tags, and Metrics.
*
* Import hub content. *
** Hub APIs are only callable through SageMaker Studio. *
** Lists the actions in your account and their properties. *
* * @param listActionsRequest * @return Result of the ListActions operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListActions * @see AWS API * Documentation */ ListActionsResult listActions(ListActionsRequest listActionsRequest); /** ** Lists the machine learning algorithms that have been created. *
* * @param listAlgorithmsRequest * @return Result of the ListAlgorithms operation returned by the service. * @sample AmazonSageMaker.ListAlgorithms * @see AWS API * Documentation */ ListAlgorithmsResult listAlgorithms(ListAlgorithmsRequest listAlgorithmsRequest); /** ** Lists the aliases of a specified image or image version. *
* * @param listAliasesRequest * @return Result of the ListAliases operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListAliases * @see AWS API * Documentation */ ListAliasesResult listAliases(ListAliasesRequest listAliasesRequest); /** ** Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or * modified time, and whether the AppImageConfig name contains a specified string. *
* * @param listAppImageConfigsRequest * @return Result of the ListAppImageConfigs operation returned by the service. * @sample AmazonSageMaker.ListAppImageConfigs * @see AWS * API Documentation */ ListAppImageConfigsResult listAppImageConfigs(ListAppImageConfigsRequest listAppImageConfigsRequest); /** ** Lists apps. *
* * @param listAppsRequest * @return Result of the ListApps operation returned by the service. * @sample AmazonSageMaker.ListApps * @see AWS API * Documentation */ ListAppsResult listApps(ListAppsRequest listAppsRequest); /** ** Lists the artifacts in your account and their properties. *
* * @param listArtifactsRequest * @return Result of the ListArtifacts operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListArtifacts * @see AWS API * Documentation */ ListArtifactsResult listArtifacts(ListArtifactsRequest listArtifactsRequest); /** ** Lists the associations in your account and their properties. *
* * @param listAssociationsRequest * @return Result of the ListAssociations operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListAssociations * @see AWS API * Documentation */ ListAssociationsResult listAssociations(ListAssociationsRequest listAssociationsRequest); /** ** Request a list of jobs. *
* * @param listAutoMLJobsRequest * @return Result of the ListAutoMLJobs operation returned by the service. * @sample AmazonSageMaker.ListAutoMLJobs * @see AWS API * Documentation */ ListAutoMLJobsResult listAutoMLJobs(ListAutoMLJobsRequest listAutoMLJobsRequest); /** ** List the candidates created for the job. *
* * @param listCandidatesForAutoMLJobRequest * @return Result of the ListCandidatesForAutoMLJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListCandidatesForAutoMLJob * @see AWS API Documentation */ ListCandidatesForAutoMLJobResult listCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest listCandidatesForAutoMLJobRequest); /** ** Gets a list of the Git repositories in your account. *
* * @param listCodeRepositoriesRequest * @return Result of the ListCodeRepositories operation returned by the service. * @sample AmazonSageMaker.ListCodeRepositories * @see AWS * API Documentation */ ListCodeRepositoriesResult listCodeRepositories(ListCodeRepositoriesRequest listCodeRepositoriesRequest); /** ** Lists model compilation jobs that satisfy various filters. *
** To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use * DescribeCompilationJob. *
* * @param listCompilationJobsRequest * @return Result of the ListCompilationJobs operation returned by the service. * @sample AmazonSageMaker.ListCompilationJobs * @see AWS * API Documentation */ ListCompilationJobsResult listCompilationJobs(ListCompilationJobsRequest listCompilationJobsRequest); /** ** Lists the contexts in your account and their properties. *
* * @param listContextsRequest * @return Result of the ListContexts operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListContexts * @see AWS API * Documentation */ ListContextsResult listContexts(ListContextsRequest listContextsRequest); /** ** Lists the data quality job definitions in your account. *
* * @param listDataQualityJobDefinitionsRequest * @return Result of the ListDataQualityJobDefinitions operation returned by the service. * @sample AmazonSageMaker.ListDataQualityJobDefinitions * @see AWS API Documentation */ ListDataQualityJobDefinitionsResult listDataQualityJobDefinitions(ListDataQualityJobDefinitionsRequest listDataQualityJobDefinitionsRequest); /** ** Returns a list of devices in the fleet. *
* * @param listDeviceFleetsRequest * @return Result of the ListDeviceFleets operation returned by the service. * @sample AmazonSageMaker.ListDeviceFleets * @see AWS API * Documentation */ ListDeviceFleetsResult listDeviceFleets(ListDeviceFleetsRequest listDeviceFleetsRequest); /** ** A list of devices. *
* * @param listDevicesRequest * @return Result of the ListDevices operation returned by the service. * @sample AmazonSageMaker.ListDevices * @see AWS API * Documentation */ ListDevicesResult listDevices(ListDevicesRequest listDevicesRequest); /** ** Lists the domains. *
* * @param listDomainsRequest * @return Result of the ListDomains operation returned by the service. * @sample AmazonSageMaker.ListDomains * @see AWS API * Documentation */ ListDomainsResult listDomains(ListDomainsRequest listDomainsRequest); /** ** Lists all edge deployment plans. *
* * @param listEdgeDeploymentPlansRequest * @return Result of the ListEdgeDeploymentPlans operation returned by the service. * @sample AmazonSageMaker.ListEdgeDeploymentPlans * @see AWS API Documentation */ ListEdgeDeploymentPlansResult listEdgeDeploymentPlans(ListEdgeDeploymentPlansRequest listEdgeDeploymentPlansRequest); /** ** Returns a list of edge packaging jobs. *
* * @param listEdgePackagingJobsRequest * @return Result of the ListEdgePackagingJobs operation returned by the service. * @sample AmazonSageMaker.ListEdgePackagingJobs * @see AWS API Documentation */ ListEdgePackagingJobsResult listEdgePackagingJobs(ListEdgePackagingJobsRequest listEdgePackagingJobsRequest); /** ** Lists endpoint configurations. *
* * @param listEndpointConfigsRequest * @return Result of the ListEndpointConfigs operation returned by the service. * @sample AmazonSageMaker.ListEndpointConfigs * @see AWS * API Documentation */ ListEndpointConfigsResult listEndpointConfigs(ListEndpointConfigsRequest listEndpointConfigsRequest); /** ** Lists endpoints. *
* * @param listEndpointsRequest * @return Result of the ListEndpoints operation returned by the service. * @sample AmazonSageMaker.ListEndpoints * @see AWS API * Documentation */ ListEndpointsResult listEndpoints(ListEndpointsRequest listEndpointsRequest); /** ** Lists all the experiments in your account. The list can be filtered to show only experiments that were created in * a specific time range. The list can be sorted by experiment name or creation time. *
* * @param listExperimentsRequest * @return Result of the ListExperiments operation returned by the service. * @sample AmazonSageMaker.ListExperiments * @see AWS API * Documentation */ ListExperimentsResult listExperiments(ListExperimentsRequest listExperimentsRequest); /** *
* List FeatureGroups based on given filter and order.
*
* Returns information about the flow definitions in your account. *
* * @param listFlowDefinitionsRequest * @return Result of the ListFlowDefinitions operation returned by the service. * @sample AmazonSageMaker.ListFlowDefinitions * @see AWS * API Documentation */ ListFlowDefinitionsResult listFlowDefinitions(ListFlowDefinitionsRequest listFlowDefinitionsRequest); /** ** List hub content versions. *
** Hub APIs are only callable through SageMaker Studio. *
** List the contents of a hub. *
** Hub APIs are only callable through SageMaker Studio. *
** List all existing hubs. *
** Hub APIs are only callable through SageMaker Studio. *
** Returns information about the human task user interfaces in your account. *
* * @param listHumanTaskUisRequest * @return Result of the ListHumanTaskUis operation returned by the service. * @sample AmazonSageMaker.ListHumanTaskUis * @see AWS API * Documentation */ ListHumanTaskUisResult listHumanTaskUis(ListHumanTaskUisRequest listHumanTaskUisRequest); /** ** Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your * account. *
* * @param listHyperParameterTuningJobsRequest * @return Result of the ListHyperParameterTuningJobs operation returned by the service. * @sample AmazonSageMaker.ListHyperParameterTuningJobs * @see AWS API Documentation */ ListHyperParameterTuningJobsResult listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest listHyperParameterTuningJobsRequest); /** ** Lists the versions of a specified image and their properties. The list can be filtered by creation time or * modified time. *
* * @param listImageVersionsRequest * @return Result of the ListImageVersions operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListImageVersions * @see AWS * API Documentation */ ListImageVersionsResult listImageVersions(ListImageVersionsRequest listImageVersionsRequest); /** ** Lists the images in your account and their properties. The list can be filtered by creation time or modified * time, and whether the image name contains a specified string. *
* * @param listImagesRequest * @return Result of the ListImages operation returned by the service. * @sample AmazonSageMaker.ListImages * @see AWS API * Documentation */ ListImagesResult listImages(ListImagesRequest listImagesRequest); /** ** Returns the list of all inference experiments. *
* * @param listInferenceExperimentsRequest * @return Result of the ListInferenceExperiments operation returned by the service. * @sample AmazonSageMaker.ListInferenceExperiments * @see AWS API Documentation */ ListInferenceExperimentsResult listInferenceExperiments(ListInferenceExperimentsRequest listInferenceExperimentsRequest); /** ** Returns a list of the subtasks for an Inference Recommender job. *
** The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types. *
* * @param listInferenceRecommendationsJobStepsRequest * @return Result of the ListInferenceRecommendationsJobSteps operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListInferenceRecommendationsJobSteps * @see AWS API Documentation */ ListInferenceRecommendationsJobStepsResult listInferenceRecommendationsJobSteps( ListInferenceRecommendationsJobStepsRequest listInferenceRecommendationsJobStepsRequest); /** ** Lists recommendation jobs that satisfy various filters. *
* * @param listInferenceRecommendationsJobsRequest * @return Result of the ListInferenceRecommendationsJobs operation returned by the service. * @sample AmazonSageMaker.ListInferenceRecommendationsJobs * @see AWS API Documentation */ ListInferenceRecommendationsJobsResult listInferenceRecommendationsJobs(ListInferenceRecommendationsJobsRequest listInferenceRecommendationsJobsRequest); /** ** Gets a list of labeling jobs. *
* * @param listLabelingJobsRequest * @return Result of the ListLabelingJobs operation returned by the service. * @sample AmazonSageMaker.ListLabelingJobs * @see AWS API * Documentation */ ListLabelingJobsResult listLabelingJobs(ListLabelingJobsRequest listLabelingJobsRequest); /** ** Gets a list of labeling jobs assigned to a specified work team. *
* * @param listLabelingJobsForWorkteamRequest * @return Result of the ListLabelingJobsForWorkteam operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListLabelingJobsForWorkteam * @see AWS API Documentation */ ListLabelingJobsForWorkteamResult listLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest listLabelingJobsForWorkteamRequest); /** ** A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage * Tracking in the Amazon SageMaker Developer Guide. *
* * @param listLineageGroupsRequest * @return Result of the ListLineageGroups operation returned by the service. * @sample AmazonSageMaker.ListLineageGroups * @see AWS * API Documentation */ ListLineageGroupsResult listLineageGroups(ListLineageGroupsRequest listLineageGroupsRequest); /** ** Lists model bias jobs definitions that satisfy various filters. *
* * @param listModelBiasJobDefinitionsRequest * @return Result of the ListModelBiasJobDefinitions operation returned by the service. * @sample AmazonSageMaker.ListModelBiasJobDefinitions * @see AWS API Documentation */ ListModelBiasJobDefinitionsResult listModelBiasJobDefinitions(ListModelBiasJobDefinitionsRequest listModelBiasJobDefinitionsRequest); /** ** List the export jobs for the Amazon SageMaker Model Card. *
* * @param listModelCardExportJobsRequest * @return Result of the ListModelCardExportJobs operation returned by the service. * @sample AmazonSageMaker.ListModelCardExportJobs * @see AWS API Documentation */ ListModelCardExportJobsResult listModelCardExportJobs(ListModelCardExportJobsRequest listModelCardExportJobsRequest); /** ** List existing versions of an Amazon SageMaker Model Card. *
* * @param listModelCardVersionsRequest * @return Result of the ListModelCardVersions operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListModelCardVersions * @see AWS API Documentation */ ListModelCardVersionsResult listModelCardVersions(ListModelCardVersionsRequest listModelCardVersionsRequest); /** ** List existing model cards. *
* * @param listModelCardsRequest * @return Result of the ListModelCards operation returned by the service. * @sample AmazonSageMaker.ListModelCards * @see AWS API * Documentation */ ListModelCardsResult listModelCards(ListModelCardsRequest listModelCardsRequest); /** ** Lists model explainability job definitions that satisfy various filters. *
* * @param listModelExplainabilityJobDefinitionsRequest * @return Result of the ListModelExplainabilityJobDefinitions operation returned by the service. * @sample AmazonSageMaker.ListModelExplainabilityJobDefinitions * @see AWS API Documentation */ ListModelExplainabilityJobDefinitionsResult listModelExplainabilityJobDefinitions( ListModelExplainabilityJobDefinitionsRequest listModelExplainabilityJobDefinitionsRequest); /** ** Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos. *
* * @param listModelMetadataRequest * @return Result of the ListModelMetadata operation returned by the service. * @sample AmazonSageMaker.ListModelMetadata * @see AWS * API Documentation */ ListModelMetadataResult listModelMetadata(ListModelMetadataRequest listModelMetadataRequest); /** ** Gets a list of the model groups in your Amazon Web Services account. *
* * @param listModelPackageGroupsRequest * @return Result of the ListModelPackageGroups operation returned by the service. * @sample AmazonSageMaker.ListModelPackageGroups * @see AWS API Documentation */ ListModelPackageGroupsResult listModelPackageGroups(ListModelPackageGroupsRequest listModelPackageGroupsRequest); /** ** Lists the model packages that have been created. *
* * @param listModelPackagesRequest * @return Result of the ListModelPackages operation returned by the service. * @sample AmazonSageMaker.ListModelPackages * @see AWS * API Documentation */ ListModelPackagesResult listModelPackages(ListModelPackagesRequest listModelPackagesRequest); /** ** Gets a list of model quality monitoring job definitions in your account. *
* * @param listModelQualityJobDefinitionsRequest * @return Result of the ListModelQualityJobDefinitions operation returned by the service. * @sample AmazonSageMaker.ListModelQualityJobDefinitions * @see AWS API Documentation */ ListModelQualityJobDefinitionsResult listModelQualityJobDefinitions(ListModelQualityJobDefinitionsRequest listModelQualityJobDefinitionsRequest); /** *
* Lists models created with the CreateModel API.
*
* Gets a list of past alerts in a model monitoring schedule. *
* * @param listMonitoringAlertHistoryRequest * @return Result of the ListMonitoringAlertHistory operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListMonitoringAlertHistory * @see AWS API Documentation */ ListMonitoringAlertHistoryResult listMonitoringAlertHistory(ListMonitoringAlertHistoryRequest listMonitoringAlertHistoryRequest); /** ** Gets the alerts for a single monitoring schedule. *
* * @param listMonitoringAlertsRequest * @return Result of the ListMonitoringAlerts operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListMonitoringAlerts * @see AWS * API Documentation */ ListMonitoringAlertsResult listMonitoringAlerts(ListMonitoringAlertsRequest listMonitoringAlertsRequest); /** ** Returns list of all monitoring job executions. *
* * @param listMonitoringExecutionsRequest * @return Result of the ListMonitoringExecutions operation returned by the service. * @sample AmazonSageMaker.ListMonitoringExecutions * @see AWS API Documentation */ ListMonitoringExecutionsResult listMonitoringExecutions(ListMonitoringExecutionsRequest listMonitoringExecutionsRequest); /** ** Returns list of all monitoring schedules. *
* * @param listMonitoringSchedulesRequest * @return Result of the ListMonitoringSchedules operation returned by the service. * @sample AmazonSageMaker.ListMonitoringSchedules * @see AWS API Documentation */ ListMonitoringSchedulesResult listMonitoringSchedules(ListMonitoringSchedulesRequest listMonitoringSchedulesRequest); /** ** Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API. *
* * @param listNotebookInstanceLifecycleConfigsRequest * @return Result of the ListNotebookInstanceLifecycleConfigs operation returned by the service. * @sample AmazonSageMaker.ListNotebookInstanceLifecycleConfigs * @see AWS API Documentation */ ListNotebookInstanceLifecycleConfigsResult listNotebookInstanceLifecycleConfigs( ListNotebookInstanceLifecycleConfigsRequest listNotebookInstanceLifecycleConfigsRequest); /** ** Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region. *
* * @param listNotebookInstancesRequest * @return Result of the ListNotebookInstances operation returned by the service. * @sample AmazonSageMaker.ListNotebookInstances * @see AWS API Documentation */ ListNotebookInstancesResult listNotebookInstances(ListNotebookInstancesRequest listNotebookInstancesRequest); /** *
* Gets a list of PipeLineExecutionStep objects.
*
* Gets a list of the pipeline executions. *
* * @param listPipelineExecutionsRequest * @return Result of the ListPipelineExecutions operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListPipelineExecutions * @see AWS API Documentation */ ListPipelineExecutionsResult listPipelineExecutions(ListPipelineExecutionsRequest listPipelineExecutionsRequest); /** ** Gets a list of parameters for a pipeline execution. *
* * @param listPipelineParametersForExecutionRequest * @return Result of the ListPipelineParametersForExecution operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListPipelineParametersForExecution * @see AWS API Documentation */ ListPipelineParametersForExecutionResult listPipelineParametersForExecution( ListPipelineParametersForExecutionRequest listPipelineParametersForExecutionRequest); /** ** Gets a list of pipelines. *
* * @param listPipelinesRequest * @return Result of the ListPipelines operation returned by the service. * @sample AmazonSageMaker.ListPipelines * @see AWS API * Documentation */ ListPipelinesResult listPipelines(ListPipelinesRequest listPipelinesRequest); /** ** Lists processing jobs that satisfy various filters. *
* * @param listProcessingJobsRequest * @return Result of the ListProcessingJobs operation returned by the service. * @sample AmazonSageMaker.ListProcessingJobs * @see AWS * API Documentation */ ListProcessingJobsResult listProcessingJobs(ListProcessingJobsRequest listProcessingJobsRequest); /** ** Gets a list of the projects in an Amazon Web Services account. *
* * @param listProjectsRequest * @return Result of the ListProjects operation returned by the service. * @sample AmazonSageMaker.ListProjects * @see AWS API * Documentation */ ListProjectsResult listProjects(ListProjectsRequest listProjectsRequest); /** *
* Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of
* ResourceCatalogs viewable is 1000.
*
* Lists spaces. *
* * @param listSpacesRequest * @return Result of the ListSpaces operation returned by the service. * @sample AmazonSageMaker.ListSpaces * @see AWS API * Documentation */ ListSpacesResult listSpaces(ListSpacesRequest listSpacesRequest); /** ** Lists devices allocated to the stage, containing detailed device information and deployment status. *
* * @param listStageDevicesRequest * @return Result of the ListStageDevices operation returned by the service. * @sample AmazonSageMaker.ListStageDevices * @see AWS API * Documentation */ ListStageDevicesResult listStageDevices(ListStageDevicesRequest listStageDevicesRequest); /** ** Lists the Studio Lifecycle Configurations in your Amazon Web Services Account. *
* * @param listStudioLifecycleConfigsRequest * @return Result of the ListStudioLifecycleConfigs operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.ListStudioLifecycleConfigs * @see AWS API Documentation */ ListStudioLifecycleConfigsResult listStudioLifecycleConfigs(ListStudioLifecycleConfigsRequest listStudioLifecycleConfigsRequest); /** *
* Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be
* empty if no work team satisfies the filter specified in the NameContains parameter.
*
* Returns the tags for the specified SageMaker resource. *
* * @param listTagsRequest * @return Result of the ListTags operation returned by the service. * @sample AmazonSageMaker.ListTags * @see AWS API * Documentation */ ListTagsResult listTags(ListTagsRequest listTagsRequest); /** ** Lists training jobs. *
*
* When StatusEquals and MaxResults are set at the same time, the MaxResults
* number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are
* filtered by the StatusEquals parameter, which is returned as a response.
*
* For example, if ListTrainingJobs is invoked with the following parameters:
*
* { ... MaxResults: 100, StatusEquals: InProgress ... }
*
* First, 100 trainings jobs with any status, including those other than InProgress, are selected
* (sorted according to the creation time, from the most current to the oldest). Next, those with a status of
* InProgress are returned.
*
* You can quickly test the API using the following Amazon Web Services CLI code. *
*
* aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress
*
* Gets a list of TrainingJobSummary * objects that describe the training jobs that a hyperparameter tuning job launched. *
* * @param listTrainingJobsForHyperParameterTuningJobRequest * @return Result of the ListTrainingJobsForHyperParameterTuningJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListTrainingJobsForHyperParameterTuningJob * @see AWS API Documentation */ ListTrainingJobsForHyperParameterTuningJobResult listTrainingJobsForHyperParameterTuningJob( ListTrainingJobsForHyperParameterTuningJobRequest listTrainingJobsForHyperParameterTuningJobRequest); /** ** Lists transform jobs. *
* * @param listTransformJobsRequest * @return Result of the ListTransformJobs operation returned by the service. * @sample AmazonSageMaker.ListTransformJobs * @see AWS * API Documentation */ ListTransformJobsResult listTransformJobs(ListTransformJobsRequest listTransformJobsRequest); /** ** Lists the trial components in your account. You can sort the list by trial component name or creation time. You * can filter the list to show only components that were created in a specific time range. You can also filter on * one of the following: *
*
* ExperimentName
*
* SourceArn
*
* TrialName
*
* Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of * that experiment. Specify a trial component name to limit the list to the trials that associated with that trial * component. The list can be filtered to show only trials that were created in a specific time range. The list can * be sorted by trial name or creation time. *
* * @param listTrialsRequest * @return Result of the ListTrials operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.ListTrials * @see AWS API * Documentation */ ListTrialsResult listTrials(ListTrialsRequest listTrialsRequest); /** ** Lists user profiles. *
* * @param listUserProfilesRequest * @return Result of the ListUserProfiles operation returned by the service. * @sample AmazonSageMaker.ListUserProfiles * @see AWS API * Documentation */ ListUserProfilesResult listUserProfiles(ListUserProfilesRequest listUserProfilesRequest); /** ** Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can * only have one private workforce per Amazon Web Services Region. *
* * @param listWorkforcesRequest * @return Result of the ListWorkforces operation returned by the service. * @sample AmazonSageMaker.ListWorkforces * @see AWS API * Documentation */ ListWorkforcesResult listWorkforces(ListWorkforcesRequest listWorkforcesRequest); /** *
* Gets a list of private work teams that you have defined in a region. The list may be empty if no work team
* satisfies the filter specified in the NameContains parameter.
*
* Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based * policies and resource-based policies in the Amazon Web Services Identity and Access Management User * Guide.. *
* * @param putModelPackageGroupPolicyRequest * @return Result of the PutModelPackageGroupPolicy operation returned by the service. * @sample AmazonSageMaker.PutModelPackageGroupPolicy * @see AWS API Documentation */ PutModelPackageGroupPolicyResult putModelPackageGroupPolicy(PutModelPackageGroupPolicyRequest putModelPackageGroupPolicyRequest); /** ** Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage * Entities in the Amazon SageMaker Developer Guide. *
* * @param queryLineageRequest * @return Result of the QueryLineage operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.QueryLineage * @see AWS API * Documentation */ QueryLineageResult queryLineage(QueryLineageRequest queryLineageRequest); /** ** Register devices. *
* * @param registerDevicesRequest * @return Result of the RegisterDevices 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.RegisterDevices * @see AWS API * Documentation */ RegisterDevicesResult registerDevices(RegisterDevicesRequest registerDevicesRequest); /** ** Renders the UI template so that you can preview the worker's experience. *
* * @param renderUiTemplateRequest * @return Result of the RenderUiTemplate operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.RenderUiTemplate * @see AWS API * Documentation */ RenderUiTemplateResult renderUiTemplate(RenderUiTemplateRequest renderUiTemplateRequest); /** ** Retry the execution of the pipeline. *
* * @param retryPipelineExecutionRequest * @return Result of the RetryPipelineExecution 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.RetryPipelineExecution
* @see AWS API Documentation
*/
RetryPipelineExecutionResult retryPipelineExecution(RetryPipelineExecutionRequest retryPipelineExecutionRequest);
/**
*
* Finds SageMaker resources that match a search query. Matching resources are returned as a list of
* SearchRecord objects in the response. You can sort the search results by any resource property in a
* ascending or descending order.
*
* You can query against the following value types: numeric, text, Boolean, and timestamp. *
** The Search API may provide access to otherwise restricted data. See Amazon SageMaker API * Permissions: Actions, Permissions, and Resources Reference for more information. *
** Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a * callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon * Simple Queue Service (Amazon SQS). *
* * @param sendPipelineExecutionStepFailureRequest * @return Result of the SendPipelineExecutionStepFailure 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.SendPipelineExecutionStepFailure * @see AWS API Documentation */ SendPipelineExecutionStepFailureResult sendPipelineExecutionStepFailure(SendPipelineExecutionStepFailureRequest sendPipelineExecutionStepFailureRequest); /** ** Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output * parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a * message sent to Amazon Simple Queue Service (Amazon SQS). *
* * @param sendPipelineExecutionStepSuccessRequest * @return Result of the SendPipelineExecutionStepSuccess 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.SendPipelineExecutionStepSuccess * @see AWS API Documentation */ SendPipelineExecutionStepSuccessResult sendPipelineExecutionStepSuccess(SendPipelineExecutionStepSuccessRequest sendPipelineExecutionStepSuccessRequest); /** ** Starts a stage in an edge deployment plan. *
* * @param startEdgeDeploymentStageRequest * @return Result of the StartEdgeDeploymentStage operation returned by the service. * @sample AmazonSageMaker.StartEdgeDeploymentStage * @see AWS API Documentation */ StartEdgeDeploymentStageResult startEdgeDeploymentStage(StartEdgeDeploymentStageRequest startEdgeDeploymentStageRequest); /** ** Starts an inference experiment. *
* * @param startInferenceExperimentRequest * @return Result of the StartInferenceExperiment operation returned by the service. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.StartInferenceExperiment
* @see AWS API Documentation
*/
StartInferenceExperimentResult startInferenceExperiment(StartInferenceExperimentRequest startInferenceExperimentRequest);
/**
* * Starts a previously stopped monitoring schedule. *
*
* By default, when you successfully create a new schedule, the status of a monitoring schedule is
* scheduled.
*
* Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
* After configuring the notebook instance, SageMaker sets the notebook instance status to InService. A
* notebook instance's status must be InService before you can connect to your Jupyter notebook.
*
* Starts a pipeline execution. *
* * @param startPipelineExecutionRequest * @return Result of the StartPipelineExecution 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.StartPipelineExecution * @see AWS API Documentation */ StartPipelineExecutionResult startPipelineExecution(StartPipelineExecutionRequest startPipelineExecutionRequest); /** ** A method for forcing a running job to shut down. *
* * @param stopAutoMLJobRequest * @return Result of the StopAutoMLJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.StopAutoMLJob * @see AWS API * Documentation */ StopAutoMLJobResult stopAutoMLJob(StopAutoMLJobRequest stopAutoMLJobRequest); /** ** Stops a model compilation job. *
** To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If * the job hasn't stopped, it sends the SIGKILL signal. *
*
* When it receives a StopCompilationJob request, Amazon SageMaker changes the
* CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it
* sets the CompilationJobStatus to Stopped.
*
* Stops a stage in an edge deployment plan. *
* * @param stopEdgeDeploymentStageRequest * @return Result of the StopEdgeDeploymentStage operation returned by the service. * @sample AmazonSageMaker.StopEdgeDeploymentStage * @see AWS API Documentation */ StopEdgeDeploymentStageResult stopEdgeDeploymentStage(StopEdgeDeploymentStageRequest stopEdgeDeploymentStageRequest); /** ** Request to stop an edge packaging job. *
* * @param stopEdgePackagingJobRequest * @return Result of the StopEdgePackagingJob operation returned by the service. * @sample AmazonSageMaker.StopEdgePackagingJob * @see AWS * API Documentation */ StopEdgePackagingJobResult stopEdgePackagingJob(StopEdgePackagingJobRequest stopEdgePackagingJobRequest); /** ** Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched. *
*
* All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All
* data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning
* job moves to the Stopped state, it releases all reserved resources for the tuning job.
*
* Stops an inference experiment. *
* * @param stopInferenceExperimentRequest * @return Result of the StopInferenceExperiment operation returned by the service. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.StopInferenceExperiment
* @see AWS API Documentation
*/
StopInferenceExperimentResult stopInferenceExperiment(StopInferenceExperimentRequest stopInferenceExperimentRequest);
/**
* * Stops an Inference Recommender job. *
* * @param stopInferenceRecommendationsJobRequest * @return Result of the StopInferenceRecommendationsJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.StopInferenceRecommendationsJob * @see AWS API Documentation */ StopInferenceRecommendationsJobResult stopInferenceRecommendationsJob(StopInferenceRecommendationsJobRequest stopInferenceRecommendationsJobRequest); /** ** Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is * stopped are placed in the Amazon S3 output bucket. *
* * @param stopLabelingJobRequest * @return Result of the StopLabelingJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.StopLabelingJob * @see AWS API * Documentation */ StopLabelingJobResult stopLabelingJob(StopLabelingJobRequest stopLabelingJobRequest); /** ** Stops a previously started monitoring schedule. *
* * @param stopMonitoringScheduleRequest * @return Result of the StopMonitoringSchedule operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.StopMonitoringSchedule * @see AWS API Documentation */ StopMonitoringScheduleResult stopMonitoringSchedule(StopMonitoringScheduleRequest stopMonitoringScheduleRequest); /** *
* Terminates the ML compute instance. Before terminating the instance, SageMaker disconnects the ML storage volume
* from it. SageMaker preserves the ML storage volume. SageMaker stops charging you for the ML compute instance when
* you call StopNotebookInstance.
*
* To access data on the ML storage volume for a notebook instance that has been terminated, call the
* StartNotebookInstance API. StartNotebookInstance launches another ML compute instance,
* configures it, and attaches the preserved ML storage volume so you can continue your work.
*
* Stops a pipeline execution. *
** Callback Step *
*
* A pipeline execution won't stop while a callback step is running. When you call
* StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines
* sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a
* "Status" field which is set to "Stopping".
*
* You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource
* cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or
* SendPipelineExecutionStepFailure.
*
* Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution. *
** Lambda Step *
*
* A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the
* lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the
* pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then
* stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is
* hit the pipeline execution status is Failed.
*
* Stops a processing job. *
* * @param stopProcessingJobRequest * @return Result of the StopProcessingJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.StopProcessingJob * @see AWS * API Documentation */ StopProcessingJobResult stopProcessingJob(StopProcessingJobRequest stopProcessingJobRequest); /** *
* Stops a training job. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays
* job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the
* results of the training is not lost.
*
* When it receives a StopTrainingJob request, SageMaker changes the status of the job to
* Stopping. After SageMaker stops the job, it sets the status to Stopped.
*
* Stops a batch transform job. *
*
* When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to
* Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you
* stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
*
* Updates an action. *
* * @param updateActionRequest * @return Result of the UpdateAction operation returned by the service. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.UpdateAction
* @see AWS API
* Documentation
*/
UpdateActionResult updateAction(UpdateActionRequest updateActionRequest);
/**
* * Updates the properties of an AppImageConfig. *
* * @param updateAppImageConfigRequest * @return Result of the UpdateAppImageConfig operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdateAppImageConfig * @see AWS * API Documentation */ UpdateAppImageConfigResult updateAppImageConfig(UpdateAppImageConfigRequest updateAppImageConfigRequest); /** ** Updates an artifact. *
* * @param updateArtifactRequest * @return Result of the UpdateArtifact operation returned by the service. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.UpdateArtifact
* @see AWS API
* Documentation
*/
UpdateArtifactResult updateArtifact(UpdateArtifactRequest updateArtifactRequest);
/**
* * Updates the specified Git repository with the specified values. *
* * @param updateCodeRepositoryRequest * @return Result of the UpdateCodeRepository operation returned by the service. * @sample AmazonSageMaker.UpdateCodeRepository * @see AWS * API Documentation */ UpdateCodeRepositoryResult updateCodeRepository(UpdateCodeRepositoryRequest updateCodeRepositoryRequest); /** ** Updates a context. *
* * @param updateContextRequest * @return Result of the UpdateContext operation returned by the service. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.UpdateContext
* @see AWS API
* Documentation
*/
UpdateContextResult updateContext(UpdateContextRequest updateContextRequest);
/**
* * Updates a fleet of devices. *
* * @param updateDeviceFleetRequest * @return Result of the UpdateDeviceFleet operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @sample AmazonSageMaker.UpdateDeviceFleet * @see AWS * API Documentation */ UpdateDeviceFleetResult updateDeviceFleet(UpdateDeviceFleetRequest updateDeviceFleetRequest); /** ** Updates one or more devices in a fleet. *
* * @param updateDevicesRequest * @return Result of the UpdateDevices operation returned by the service. * @sample AmazonSageMaker.UpdateDevices * @see AWS API * Documentation */ UpdateDevicesResult updateDevices(UpdateDevicesRequest updateDevicesRequest); /** ** Updates the default settings for new user profiles in the domain. *
* * @param updateDomainRequest * @return Result of the UpdateDomain 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. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdateDomain * @see AWS API * Documentation */ UpdateDomainResult updateDomain(UpdateDomainRequest updateDomainRequest); /** *
* Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint,
* and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is
* no availability loss).
*
* When SageMaker receives the request, it sets the endpoint status to Updating. After updating the
* endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint
* API.
*
* 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. To
* update an endpoint, you must create a new EndpointConfig.
*
* 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.
*
* Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant
* associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to
* Updating. After updating the endpoint, it sets the status to InService. To check the
* status of an endpoint, use the DescribeEndpoint
* API.
*
* Adds, updates, or removes the description of an experiment. Updates the display name of an experiment. *
* * @param updateExperimentRequest * @return Result of the UpdateExperiment operation returned by the service. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.UpdateExperiment
* @see AWS API
* Documentation
*/
UpdateExperimentResult updateExperiment(UpdateExperimentRequest updateExperimentRequest);
/**
*
* Updates the feature group by either adding features or updating the online store configuration. Use one of the
* following request parameters at a time while using the UpdateFeatureGroup API.
*
* You can add features for your feature group using the FeatureAdditions request parameter. Features
* cannot be removed from a feature group.
*
* You can update the online store configuration by using the OnlineStoreConfig request parameter. If a
* TtlDuration is specified, the default TtlDuration applies for all records added to the
* feature group after the feature group is updated. If a record level TtlDuration exists from
* using the PutRecord API, the record level TtlDuration applies to that record instead of
* the default TtlDuration.
*
* Updates the description and parameters of the feature group. *
* * @param updateFeatureMetadataRequest * @return Result of the UpdateFeatureMetadata operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdateFeatureMetadata * @see AWS API Documentation */ UpdateFeatureMetadataResult updateFeatureMetadata(UpdateFeatureMetadataRequest updateFeatureMetadataRequest); /** ** Update a hub. *
** Hub APIs are only callable through SageMaker Studio. *
** Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs. *
* * @param updateImageRequest * @return Result of the UpdateImage operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdateImage * @see AWS API * Documentation */ UpdateImageResult updateImage(UpdateImageRequest updateImageRequest); /** ** Updates the properties of a SageMaker image version. *
* * @param updateImageVersionRequest * @return Result of the UpdateImageVersion operation returned by the service. * @throws ResourceInUseException * Resource being accessed is in use. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdateImageVersion * @see AWS * API Documentation */ UpdateImageVersionResult updateImageVersion(UpdateImageVersionRequest updateImageVersionRequest); /** *
* Updates an inference experiment that you created. The status of the inference experiment has to be either
* Created, Running. For more information on the status of an inference experiment, see
* DescribeInferenceExperiment.
*
Experiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.UpdateInferenceExperiment
* @see AWS API Documentation
*/
UpdateInferenceExperimentResult updateInferenceExperiment(UpdateInferenceExperimentRequest updateInferenceExperimentRequest);
/**
* * Update an Amazon SageMaker Model Card. *
** You cannot update both model card content and model card status in a single call. *
*Experiment
* or Artifact.
* @sample AmazonSageMaker.UpdateModelCard
* @see AWS API
* Documentation
*/
UpdateModelCardResult updateModelCard(UpdateModelCardRequest updateModelCardRequest);
/**
* * Updates a versioned model. *
* * @param updateModelPackageRequest * @return Result of the UpdateModelPackage operation returned by the service. * @sample AmazonSageMaker.UpdateModelPackage * @see AWS * API Documentation */ UpdateModelPackageResult updateModelPackage(UpdateModelPackageRequest updateModelPackageRequest); /** ** Update the parameters of a model monitor alert. *
* * @param updateMonitoringAlertRequest * @return Result of the UpdateMonitoringAlert 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 ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdateMonitoringAlert * @see AWS API Documentation */ UpdateMonitoringAlertResult updateMonitoringAlert(UpdateMonitoringAlertRequest updateMonitoringAlertRequest); /** ** Updates a previously created schedule. *
* * @param updateMonitoringScheduleRequest * @return Result of the UpdateMonitoringSchedule 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 ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdateMonitoringSchedule * @see AWS API Documentation */ UpdateMonitoringScheduleResult updateMonitoringSchedule(UpdateMonitoringScheduleRequest updateMonitoringScheduleRequest); /** ** Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance * used for your notebook instance to accommodate changes in your workload requirements. *
* * @param updateNotebookInstanceRequest * @return Result of the UpdateNotebookInstance 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.UpdateNotebookInstance * @see AWS API Documentation */ UpdateNotebookInstanceResult updateNotebookInstance(UpdateNotebookInstanceRequest updateNotebookInstanceRequest); /** ** Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API. *
* * @param updateNotebookInstanceLifecycleConfigRequest * @return Result of the UpdateNotebookInstanceLifecycleConfig 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.UpdateNotebookInstanceLifecycleConfig * @see AWS API Documentation */ UpdateNotebookInstanceLifecycleConfigResult updateNotebookInstanceLifecycleConfig( UpdateNotebookInstanceLifecycleConfigRequest updateNotebookInstanceLifecycleConfigRequest); /** ** Updates a pipeline. *
* * @param updatePipelineRequest * @return Result of the UpdatePipeline operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdatePipeline * @see AWS API * Documentation */ UpdatePipelineResult updatePipeline(UpdatePipelineRequest updatePipelineRequest); /** ** Updates a pipeline execution. *
* * @param updatePipelineExecutionRequest * @return Result of the UpdatePipelineExecution operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdatePipelineExecution * @see AWS API Documentation */ UpdatePipelineExecutionResult updatePipelineExecution(UpdatePipelineExecutionRequest updatePipelineExecutionRequest); /** ** Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training * to deploying an approved model. *
*
* You must not update a project that is in use. If you update the
* ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated,
* you may lose resources already created by the project.
*
* Updates the settings of a space. *
* * @param updateSpaceRequest * @return Result of the UpdateSpace 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. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdateSpace * @see AWS API * Documentation */ UpdateSpaceResult updateSpace(UpdateSpaceRequest updateSpaceRequest); /** ** Update a model training job to request a new Debugger profiling configuration or to change warm pool retention * length. *
* * @param updateTrainingJobRequest * @return Result of the UpdateTrainingJob operation returned by the service. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdateTrainingJob * @see AWS * API Documentation */ UpdateTrainingJobResult updateTrainingJob(UpdateTrainingJobRequest updateTrainingJobRequest); /** ** Updates the display name of a trial. *
* * @param updateTrialRequest * @return Result of the UpdateTrial operation returned by the service. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.UpdateTrial
* @see AWS API
* Documentation
*/
UpdateTrialResult updateTrial(UpdateTrialRequest updateTrialRequest);
/**
* * Updates one or more properties of a trial component. *
* * @param updateTrialComponentRequest * @return Result of the UpdateTrialComponent operation returned by the service. * @throws ConflictException * There was a conflict when you attempted to modify a SageMaker entity such as anExperiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.UpdateTrialComponent
* @see AWS
* API Documentation
*/
UpdateTrialComponentResult updateTrialComponent(UpdateTrialComponentRequest updateTrialComponentRequest);
/**
* * Updates a user profile. *
* * @param updateUserProfileRequest * @return Result of the UpdateUserProfile 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. * @throws ResourceNotFoundException * Resource being access is not found. * @sample AmazonSageMaker.UpdateUserProfile * @see AWS * API Documentation */ UpdateUserProfileResult updateUserProfile(UpdateUserProfileRequest updateUserProfileRequest); /** ** Use this operation to update your workforce. You can use this operation to require that workers use specific IP * addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce * configuration. *
** The worker portal is now supported in VPC and public internet. *
*
* Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You
* specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't
* restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks
* using any IP address outside the specified range are denied and get a Not Found error message on the
* worker portal.
*
* To restrict access to all the workers in public internet, add the SourceIpConfig CIDR value as
* "10.0.0.0/16".
*
* Amazon SageMaker does not support Source Ip restriction for worker portals in VPC. *
*
* Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP.
*
* You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You * can delete work teams using the DeleteWorkteam * operation. *
** After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, * you can view details about your update workforce using the DescribeWorkforce * operation. *
** This operation only applies to private workforces. *
*Experiment
* or Artifact.
* @sample AmazonSageMaker.UpdateWorkforce
* @see AWS API
* Documentation
*/
UpdateWorkforceResult updateWorkforce(UpdateWorkforceRequest updateWorkforceRequest);
/**
* * Updates an existing work team with new member definitions or description. *
* * @param updateWorkteamRequest * @return Result of the UpdateWorkteam 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.UpdateWorkteam * @see AWS API * Documentation */ UpdateWorkteamResult updateWorkteam(UpdateWorkteamRequest updateWorkteamRequest); /** * Shuts down this client object, releasing any resources that might be held open. This is an optional method, and * callers are not expected to call it, but can if they want to explicitly release any open resources. Once a client * has been shutdown, it should not be used to make any more requests. */ void shutdown(); /** * Returns additional metadata for a previously executed successful request, typically used for debugging issues * where a service isn't acting as expected. This data isn't considered part of the result data returned by an * operation, so it's available through this separate, diagnostic interface. ** Response metadata is only cached for a limited period of time, so if you need to access this extra diagnostic * information for an executed request, you should use this method to retrieve it as soon as possible after * executing a request. * * @param request * The originally executed request. * * @return The response metadata for the specified request, or null if none is available. */ ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request); AmazonSageMakerWaiters waiters(); }