com.amazonaws.services.sagemaker.AmazonSageMaker Maven / Gradle / Ivy
/*
* Copyright 2019-2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.sagemaker;
import javax.annotation.Generated;
import com.amazonaws.*;
import com.amazonaws.regions.*;
import com.amazonaws.services.sagemaker.model.*;
import com.amazonaws.services.sagemaker.waiters.AmazonSageMakerWaiters;
/**
* Interface for accessing SageMaker.
*
* 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:
*
*
* -
*
*
* -
*
*
*
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public interface AmazonSageMaker {
/**
* The region metadata service name for computing region endpoints. You can use this value to retrieve metadata
* (such as supported regions) of the service.
*
* @see RegionUtils#getRegionsForService(String)
*/
String ENDPOINT_PREFIX = "api.sagemaker";
/**
*
* 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 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.
*
*
*
* @param addTagsRequest
* @return Result of the AddTags operation returned by the service.
* @sample AmazonSageMaker.AddTags
* @see AWS API
* Documentation
*/
AddTagsResult addTags(AddTagsRequest addTagsRequest);
/**
*
* 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
* 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 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 time-series forecasting, non-tabular problem types such as image or text
* classification, and text generation (LLMs fine-tuning).
*
*
* 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 time-series forecasting, non-tabular problem types such as image or text
* classification, and text generation (LLMs fine-tuning).
*
*
* 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 SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing
* persistent clusters for developing large machine learning models, such as large language models (LLMs) and
* diffusion models. To learn more, see Amazon SageMaker HyperPod in
* the Amazon SageMaker Developer Guide.
*
*
* @param createClusterRequest
* @return Result of the CreateCluster 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.CreateCluster
* @see AWS API
* Documentation
*/
CreateClusterResult createCluster(CreateClusterRequest createClusterRequest);
/**
*
* 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. A domain consists of an associated Amazon Elastic File System 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 traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other
* traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType
* corresponds to the network access type that you choose when you onboard to the domain. 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 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 Amazon SageMaker 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
* Amazon SageMaker Studio app successfully.
*
*
*
* For more information, see Connect Amazon
* 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.
*
*
*
* 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.
*
*
*
*
*
* @param createEndpointRequest
* @return Result of the CreateEndpoint 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.CreateEndpoint
* @see AWS API
* Documentation
*/
CreateEndpointResult createEndpoint(CreateEndpointRequest createEndpointRequest);
/**
*
* 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.
*
*
*
* @param createEndpointConfigRequest
* @return Result of the CreateEndpointConfig 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.CreateEndpointConfig
* @see AWS
* API Documentation
*/
CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest createEndpointConfigRequest);
/**
*
* 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.
*
*
* Note that it can take approximately 10-15 minutes to provision an OnlineStore
* FeatureGroup with the InMemory StorageType.
*
*
*
* You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a
* FeatureGroup.
*
*
*
* @param createFeatureGroupRequest
* @return Result of the CreateFeatureGroup 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.CreateFeatureGroup
* @see AWS
* API Documentation
*/
CreateFeatureGroupResult createFeatureGroup(CreateFeatureGroupRequest createFeatureGroupRequest);
/**
*
* 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.
*
*
* @param createHubRequest
* @return Result of the CreateHub 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.CreateHub
* @see AWS API
* Documentation
*/
CreateHubResult createHub(CreateHubRequest createHubRequest);
/**
*
* Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
*
*
* @param createHubContentReferenceRequest
* @return Result of the CreateHubContentReference operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @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.CreateHubContentReference
* @see AWS API Documentation
*/
CreateHubContentReferenceResult createHubContentReference(CreateHubContentReferenceRequest createHubContentReferenceRequest);
/**
*
* 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.
*
*
*
* @param createHyperParameterTuningJobRequest
* @return Result of the CreateHyperParameterTuningJob 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.CreateHyperParameterTuningJob
* @see AWS API Documentation
*/
CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest);
/**
*
* Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a
* container image stored in Amazon 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
* ECR container image specified by BaseImage.
*
*
* @param createImageVersionRequest
* @return Result of the CreateImageVersion 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.CreateImageVersion
* @see AWS
* API Documentation
*/
CreateImageVersionResult createImageVersion(CreateImageVersionRequest createImageVersionRequest);
/**
*
* Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an
* endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes
* the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU
* cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where
* each inference component contains one model and the resource utilization needs for that individual model. After
* you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint
* API action.
*
*
* @param createInferenceComponentRequest
* @return Result of the CreateInferenceComponent 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.CreateInferenceComponent
* @see AWS API Documentation
*/
CreateInferenceComponentResult createInferenceComponent(CreateInferenceComponentRequest createInferenceComponentRequest);
/**
*
* 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.
*
*
* @param createLabelingJobRequest
* @return Result of the CreateLabelingJob 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.CreateLabelingJob
* @see AWS
* API Documentation
*/
CreateLabelingJobResult createLabelingJob(CreateLabelingJobRequest createLabelingJobRequest);
/**
*
* Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more
* information, see Create an MLflow
* Tracking Server.
*
*
* @param createMlflowTrackingServerRequest
* @return Result of the CreateMlflowTrackingServer 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.CreateMlflowTrackingServer
* @see AWS API Documentation
*/
CreateMlflowTrackingServerResult createMlflowTrackingServer(CreateMlflowTrackingServerRequest createMlflowTrackingServerRequest);
/**
*
* 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.
*
*
* 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 an Experiment
* 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 an Experiment
* 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.
*
*
*
*
*
* @param createModelPackageRequest
* @return Result of the CreateModelPackage operation returned by the service.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an 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 Amazon 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 job that optimizes a model for inference performance. To create the job, you provide the location of a
* source model, and you provide the settings for the optimization techniques that you want the job to apply. When
* the job completes successfully, SageMaker uploads the new optimized model to the output destination that you
* specify.
*
*
* For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon
* SageMaker.
*
*
* @param createOptimizationJobRequest
* @return Result of the CreateOptimizationJob 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.CreateOptimizationJob
* @see AWS API Documentation
*/
CreateOptimizationJobResult createOptimizationJob(CreateOptimizationJobRequest createOptimizationJobRequest);
/**
*
* 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.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @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 the domain, and granted access to all of the Apps and files associated with the
* Domain's Amazon Elastic File System 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 Amazon 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.
*
*
*
* @param createPresignedDomainUrlRequest
* @return Result of the CreatePresignedDomainUrl operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.CreatePresignedDomainUrl
* @see AWS API Documentation
*/
CreatePresignedDomainUrlResult createPresignedDomainUrl(CreatePresignedDomainUrlRequest createPresignedDomainUrlRequest);
/**
*
* Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more
* information, see Launch the
* MLflow UI using a presigned URL.
*
*
* @param createPresignedMlflowTrackingServerUrlRequest
* @return Result of the CreatePresignedMlflowTrackingServerUrl operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.CreatePresignedMlflowTrackingServerUrl
* @see AWS API Documentation
*/
CreatePresignedMlflowTrackingServerUrlResult createPresignedMlflowTrackingServerUrl(
CreatePresignedMlflowTrackingServerUrlRequest createPresignedMlflowTrackingServerUrlRequest);
/**
*
* 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.
*
*
*
* @param createPresignedNotebookInstanceUrlRequest
* @return Result of the CreatePresignedNotebookInstanceUrl operation returned by the service.
* @sample AmazonSageMaker.CreatePresignedNotebookInstanceUrl
* @see AWS API Documentation
*/
CreatePresignedNotebookInstanceUrlResult createPresignedNotebookInstanceUrl(
CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest);
/**
*
* 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 private space or 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 Amazon SageMaker 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 a domain. 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 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).
*
*
* @param createWorkforceRequest
* @return Result of the CreateWorkforce operation returned by the service.
* @sample AmazonSageMaker.CreateWorkforce
* @see AWS API
* Documentation
*/
CreateWorkforceResult createWorkforce(CreateWorkforceRequest createWorkforceRequest);
/**
*
* 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.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @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.
*
*
* @param deleteArtifactRequest
* @return Result of the DeleteArtifact operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteArtifact
* @see AWS API
* Documentation
*/
DeleteArtifactResult deleteArtifact(DeleteArtifactRequest deleteArtifactRequest);
/**
*
* 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);
/**
*
* Delete a SageMaker HyperPod cluster.
*
*
* @param deleteClusterRequest
* @return Result of the DeleteCluster 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 an Experiment
* or Artifact.
* @sample AmazonSageMaker.DeleteCluster
* @see AWS API
* Documentation
*/
DeleteClusterResult deleteCluster(DeleteClusterRequest deleteClusterRequest);
/**
*
* 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 the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker.
* It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates,
* the compilation logs in CloudWatch, the compiled model, or the IAM role.
*
*
* You can delete a compilation job only if its current status is COMPLETED, FAILED, or
* STOPPED. If the job status is STARTING or INPROGRESS, stop the job, and
* then delete it after its status becomes STOPPED.
*
*
* @param deleteCompilationJobRequest
* @return Result of the DeleteCompilationJob operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteCompilationJob
* @see AWS
* API Documentation
*/
DeleteCompilationJobResult deleteCompilationJob(DeleteCompilationJobRequest deleteCompilationJobRequest);
/**
*
* 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.
*
*
* @param deleteEndpointRequest
* @return Result of the DeleteEndpoint operation returned by the service.
* @sample AmazonSageMaker.DeleteEndpoint
* @see AWS API
* Documentation
*/
DeleteEndpointResult deleteEndpoint(DeleteEndpointRequest deleteEndpointRequest);
/**
*
* Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified
* configuration. It does not delete endpoints created using the configuration.
*
*
* You must not delete an EndpointConfig in use by an endpoint that is live or while the
* UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you
* delete the EndpointConfig of an endpoint that is active or being created or updated you may lose
* visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring
* charges.
*
*
* @param deleteEndpointConfigRequest
* @return Result of the DeleteEndpointConfig operation returned by the service.
* @sample AmazonSageMaker.DeleteEndpointConfig
* @see AWS
* API Documentation
*/
DeleteEndpointConfigResult deleteEndpointConfig(DeleteEndpointConfigRequest deleteEndpointConfigRequest);
/**
*
* 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.
*
*
* Note that it can take approximately 10-15 minutes to delete an OnlineStore FeatureGroup
* with the InMemory StorageType.
*
*
* @param deleteFeatureGroupRequest
* @return Result of the DeleteFeatureGroup operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteFeatureGroup
* @see AWS
* API Documentation
*/
DeleteFeatureGroupResult deleteFeatureGroup(DeleteFeatureGroupRequest deleteFeatureGroupRequest);
/**
*
* 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.
*
*
* @param deleteHubRequest
* @return Result of the DeleteHub operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteHub
* @see AWS API
* Documentation
*/
DeleteHubResult deleteHub(DeleteHubRequest deleteHubRequest);
/**
*
* Delete the contents of a hub.
*
*
* @param deleteHubContentRequest
* @return Result of the DeleteHubContent operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteHubContent
* @see AWS API
* Documentation
*/
DeleteHubContentResult deleteHubContent(DeleteHubContentRequest deleteHubContentRequest);
/**
*
* Delete a hub content reference in order to remove a model from a private hub.
*
*
* @param deleteHubContentReferenceRequest
* @return Result of the DeleteHubContentReference operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteHubContentReference
* @see AWS API Documentation
*/
DeleteHubContentReferenceResult deleteHubContentReference(DeleteHubContentReferenceRequest deleteHubContentReferenceRequest);
/**
*
* 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.
*
*
* @param deleteHumanTaskUiRequest
* @return Result of the DeleteHumanTaskUi operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteHumanTaskUi
* @see AWS
* API Documentation
*/
DeleteHumanTaskUiResult deleteHumanTaskUi(DeleteHumanTaskUiRequest deleteHumanTaskUiRequest);
/**
*
* Deletes a hyperparameter tuning job. The DeleteHyperParameterTuningJob API deletes only the tuning
* job entry that was created in SageMaker when you called the CreateHyperParameterTuningJob API. It
* does not delete training jobs, artifacts, or the IAM role that you specified when creating the model.
*
*
* @param deleteHyperParameterTuningJobRequest
* @return Result of the DeleteHyperParameterTuningJob operation returned by the service.
* @sample AmazonSageMaker.DeleteHyperParameterTuningJob
* @see AWS API Documentation
*/
DeleteHyperParameterTuningJobResult deleteHyperParameterTuningJob(DeleteHyperParameterTuningJobRequest deleteHyperParameterTuningJobRequest);
/**
*
* 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 component.
*
*
* @param deleteInferenceComponentRequest
* @return Result of the DeleteInferenceComponent operation returned by the service.
* @sample AmazonSageMaker.DeleteInferenceComponent
* @see AWS API Documentation
*/
DeleteInferenceComponentResult deleteInferenceComponent(DeleteInferenceComponentRequest deleteInferenceComponentRequest);
/**
*
* 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.
*
*
*
* @param deleteInferenceExperimentRequest
* @return Result of the DeleteInferenceExperiment operation returned by the service.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteInferenceExperiment
* @see AWS API Documentation
*/
DeleteInferenceExperimentResult deleteInferenceExperiment(DeleteInferenceExperimentRequest deleteInferenceExperimentRequest);
/**
*
* Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.
*
*
* @param deleteMlflowTrackingServerRequest
* @return Result of the DeleteMlflowTrackingServer operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteMlflowTrackingServer
* @see AWS API Documentation
*/
DeleteMlflowTrackingServerResult deleteMlflowTrackingServer(DeleteMlflowTrackingServerRequest deleteMlflowTrackingServerRequest);
/**
*
* 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.
*
*
* @param deleteModelRequest
* @return Result of the DeleteModel operation returned by the service.
* @sample AmazonSageMaker.DeleteModel
* @see AWS API
* Documentation
*/
DeleteModelResult deleteModel(DeleteModelRequest deleteModelRequest);
/**
*
* 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 an Experiment
* 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 an Experiment
* 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 an Experiment
* 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.
*
*
*
* @param deleteNotebookInstanceRequest
* @return Result of the DeleteNotebookInstance operation returned by the service.
* @sample AmazonSageMaker.DeleteNotebookInstance
* @see AWS API Documentation
*/
DeleteNotebookInstanceResult deleteNotebookInstance(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest);
/**
*
* 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 an optimization job.
*
*
* @param deleteOptimizationJobRequest
* @return Result of the DeleteOptimizationJob operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteOptimizationJob
* @see AWS API Documentation
*/
DeleteOptimizationJobResult deleteOptimizationJob(DeleteOptimizationJobRequest deleteOptimizationJobRequest);
/**
*
* 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.
*
*
* @param deletePipelineRequest
* @return Result of the DeletePipeline 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 an Experiment
* or Artifact.
* @sample AmazonSageMaker.DeletePipeline
* @see AWS API
* Documentation
*/
DeletePipelineResult deletePipeline(DeletePipelineRequest deletePipelineRequest);
/**
*
* 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 an Experiment
* 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 Amazon SageMaker 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 Domain or User Profile, the deleted tags are not removed
* from Apps that the SageMaker Domain or User Profile launched before you called this API.
*
*
*
* @param deleteTagsRequest
* @return Result of the DeleteTags operation returned by the service.
* @sample AmazonSageMaker.DeleteTags
* @see AWS API
* Documentation
*/
DeleteTagsResult deleteTags(DeleteTagsRequest deleteTagsRequest);
/**
*
* 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 receive a ResourceInUse error.
*
*
*
* @param deleteWorkforceRequest
* @return Result of the DeleteWorkforce operation returned by the service.
* @sample AmazonSageMaker.DeleteWorkforce
* @see AWS API
* Documentation
*/
DeleteWorkforceResult deleteWorkforce(DeleteWorkforceRequest deleteWorkforceRequest);
/**
*
* 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.
*
*
*
* @param describeAutoMLJobRequest
* @return Result of the DescribeAutoMLJob operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DescribeAutoMLJob
* @see AWS
* API Documentation
*/
DescribeAutoMLJobResult describeAutoMLJob(DescribeAutoMLJobRequest describeAutoMLJobRequest);
/**
*
* 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);
/**
*
* Retrieves information of a SageMaker HyperPod cluster.
*
*
* @param describeClusterRequest
* @return Result of the DescribeCluster operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DescribeCluster
* @see AWS API
* Documentation
*/
DescribeClusterResult describeCluster(DescribeClusterRequest describeClusterRequest);
/**
*
* Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
*
*
* @param describeClusterNodeRequest
* @return Result of the DescribeClusterNode operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DescribeClusterNode
* @see AWS
* API Documentation
*/
DescribeClusterNodeResult describeClusterNode(DescribeClusterNodeRequest describeClusterNodeRequest);
/**
*
* 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.
*
*
* @param describeEndpointConfigRequest
* @return Result of the DescribeEndpointConfig operation returned by the service.
* @sample AmazonSageMaker.DescribeEndpointConfig
* @see AWS API Documentation
*/
DescribeEndpointConfigResult describeEndpointConfig(DescribeEndpointConfigRequest describeEndpointConfigRequest);
/**
*
* 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.
*
*
* @param describeFeatureGroupRequest
* @return Result of the DescribeFeatureGroup operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DescribeFeatureGroup
* @see AWS
* API Documentation
*/
DescribeFeatureGroupResult describeFeatureGroup(DescribeFeatureGroupRequest describeFeatureGroupRequest);
/**
*
* 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);
/**
*
* Describes a hub.
*
*
* @param describeHubRequest
* @return Result of the DescribeHub operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DescribeHub
* @see AWS API
* Documentation
*/
DescribeHubResult describeHub(DescribeHubRequest describeHubRequest);
/**
*
* Describe the content of a hub.
*
*
* @param describeHubContentRequest
* @return Result of the DescribeHubContent operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DescribeHubContent
* @see AWS
* API Documentation
*/
DescribeHubContentResult describeHubContent(DescribeHubContentRequest describeHubContentRequest);
/**
*
* 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 information about an inference component.
*
*
* @param describeInferenceComponentRequest
* @return Result of the DescribeInferenceComponent operation returned by the service.
* @sample AmazonSageMaker.DescribeInferenceComponent
* @see AWS API Documentation
*/
DescribeInferenceComponentResult describeInferenceComponent(DescribeInferenceComponentRequest describeInferenceComponentRequest);
/**
*
* 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);
/**
*
* Returns information about an MLflow Tracking Server.
*
*
* @param describeMlflowTrackingServerRequest
* @return Result of the DescribeMlflowTrackingServer operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DescribeMlflowTrackingServer
* @see AWS API Documentation
*/
DescribeMlflowTrackingServerResult describeMlflowTrackingServer(DescribeMlflowTrackingServerRequest describeMlflowTrackingServerRequest);
/**
*
* Describes a model that you created using the CreateModel API.
*
*
* @param describeModelRequest
* @return Result of the DescribeModel operation returned by the service.
* @sample AmazonSageMaker.DescribeModel
* @see AWS API
* Documentation
*/
DescribeModelResult describeModel(DescribeModelRequest describeModelRequest);
/**
*
* 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.
*
*
*
* If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your
* CloudTrail logs when you use this API.
*
*
*
* 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);
/**
*
* Provides the properties of the specified optimization job.
*
*
* @param describeOptimizationJobRequest
* @return Result of the DescribeOptimizationJob operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DescribeOptimizationJob
* @see AWS API Documentation
*/
DescribeOptimizationJobResult describeOptimizationJob(DescribeOptimizationJobRequest describeOptimizationJobRequest);
/**
*
* 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 Amazon SageMaker 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.
*
*
* @param describeTrainingJobRequest
* @return Result of the DescribeTrainingJob operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DescribeTrainingJob
* @see AWS
* API Documentation
*/
DescribeTrainingJobResult describeTrainingJob(DescribeTrainingJobRequest describeTrainingJobRequest);
/**
*
* 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.
*
*
* @param describeUserProfileRequest
* @return Result of the DescribeUserProfile 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.DescribeUserProfile
* @see AWS
* API Documentation
*/
DescribeUserProfileResult describeUserProfile(DescribeUserProfileRequest describeUserProfileRequest);
/**
*
* 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.
*
*
*
* @param describeWorkforceRequest
* @return Result of the DescribeWorkforce operation returned by the service.
* @sample AmazonSageMaker.DescribeWorkforce
* @see AWS
* API Documentation
*/
DescribeWorkforceResult describeWorkforce(DescribeWorkforceRequest describeWorkforceRequest);
/**
*
* Gets information about a specific work team. You can see information such as the creation 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.
*
*
* @param disassociateTrialComponentRequest
* @return Result of the DisassociateTrialComponent operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DisassociateTrialComponent
* @see AWS API Documentation
*/
DisassociateTrialComponentResult disassociateTrialComponent(DisassociateTrialComponentRequest disassociateTrialComponentRequest);
/**
*
* 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);
/**
*
* Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for
* autoscaling policies that you can apply to your SageMaker endpoint.
*
*
* @param getScalingConfigurationRecommendationRequest
* @return Result of the GetScalingConfigurationRecommendation operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.GetScalingConfigurationRecommendation
* @see AWS API Documentation
*/
GetScalingConfigurationRecommendationResult getScalingConfigurationRecommendation(
GetScalingConfigurationRecommendationRequest getScalingConfigurationRecommendationRequest);
/**
*
* 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.
*
*
* @param getSearchSuggestionsRequest
* @return Result of the GetSearchSuggestions operation returned by the service.
* @sample AmazonSageMaker.GetSearchSuggestions
* @see AWS
* API Documentation
*/
GetSearchSuggestionsResult getSearchSuggestions(GetSearchSuggestionsRequest getSearchSuggestionsRequest);
/**
*
* Import hub content.
*
*
* @param importHubContentRequest
* @return Result of the ImportHubContent 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.ImportHubContent
* @see AWS API
* Documentation
*/
ImportHubContentResult importHubContent(ImportHubContentRequest importHubContentRequest);
/**
*
* 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);
/**
*
* Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
*
*
* @param listClusterNodesRequest
* @return Result of the ListClusterNodes operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.ListClusterNodes
* @see AWS API
* Documentation
*/
ListClusterNodesResult listClusterNodes(ListClusterNodesRequest listClusterNodesRequest);
/**
*
* Retrieves the list of SageMaker HyperPod clusters.
*
*
* @param listClustersRequest
* @return Result of the ListClusters operation returned by the service.
* @sample AmazonSageMaker.ListClusters
* @see AWS API
* Documentation
*/
ListClustersResult listClusters(ListClustersRequest listClustersRequest);
/**
*
* 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.
*
*
* @param listFeatureGroupsRequest
* @return Result of the ListFeatureGroups operation returned by the service.
* @sample AmazonSageMaker.ListFeatureGroups
* @see AWS
* API Documentation
*/
ListFeatureGroupsResult listFeatureGroups(ListFeatureGroupsRequest listFeatureGroupsRequest);
/**
*
* 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.
*
*
* @param listHubContentVersionsRequest
* @return Result of the ListHubContentVersions operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.ListHubContentVersions
* @see AWS API Documentation
*/
ListHubContentVersionsResult listHubContentVersions(ListHubContentVersionsRequest listHubContentVersionsRequest);
/**
*
* List the contents of a hub.
*
*
* @param listHubContentsRequest
* @return Result of the ListHubContents operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.ListHubContents
* @see AWS API
* Documentation
*/
ListHubContentsResult listHubContents(ListHubContentsRequest listHubContentsRequest);
/**
*
* List all existing hubs.
*
*
* @param listHubsRequest
* @return Result of the ListHubs operation returned by the service.
* @sample AmazonSageMaker.ListHubs
* @see AWS API
* Documentation
*/
ListHubsResult listHubs(ListHubsRequest listHubsRequest);
/**
*
* 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);
/**
*
* Lists the inference components in your account and their properties.
*
*
* @param listInferenceComponentsRequest
* @return Result of the ListInferenceComponents operation returned by the service.
* @sample AmazonSageMaker.ListInferenceComponents
* @see AWS API Documentation
*/
ListInferenceComponentsResult listInferenceComponents(ListInferenceComponentsRequest listInferenceComponentsRequest);
/**
*
* 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 all MLflow Tracking Servers.
*
*
* @param listMlflowTrackingServersRequest
* @return Result of the ListMlflowTrackingServers operation returned by the service.
* @sample AmazonSageMaker.ListMlflowTrackingServers
* @see AWS API Documentation
*/
ListMlflowTrackingServersResult listMlflowTrackingServers(ListMlflowTrackingServersRequest listMlflowTrackingServersRequest);
/**
*
* 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.
*
*
* @param listModelsRequest
* @return Result of the ListModels operation returned by the service.
* @sample AmazonSageMaker.ListModels
* @see AWS API
* Documentation
*/
ListModelsResult listModels(ListModelsRequest listModelsRequest);
/**
*
* 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);
/**
*
* Lists the optimization jobs in your account and their properties.
*
*
* @param listOptimizationJobsRequest
* @return Result of the ListOptimizationJobs operation returned by the service.
* @sample AmazonSageMaker.ListOptimizationJobs
* @see AWS
* API Documentation
*/
ListOptimizationJobsResult listOptimizationJobs(ListOptimizationJobsRequest listOptimizationJobsRequest);
/**
*
* Gets a list of PipeLineExecutionStep objects.
*
*
* @param listPipelineExecutionStepsRequest
* @return Result of the ListPipelineExecutionSteps operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.ListPipelineExecutionSteps
* @see AWS API Documentation
*/
ListPipelineExecutionStepsResult listPipelineExecutionSteps(ListPipelineExecutionStepsRequest listPipelineExecutionStepsRequest);
/**
*
* 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.
*
*
* @param listResourceCatalogsRequest
* @return Result of the ListResourceCatalogs operation returned by the service.
* @sample AmazonSageMaker.ListResourceCatalogs
* @see AWS
* API Documentation
*/
ListResourceCatalogsResult listResourceCatalogs(ListResourceCatalogsRequest listResourceCatalogsRequest);
/**
*
* 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 Amazon SageMaker 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.
*
*
* @param listSubscribedWorkteamsRequest
* @return Result of the ListSubscribedWorkteams operation returned by the service.
* @sample AmazonSageMaker.ListSubscribedWorkteams
* @see AWS API Documentation
*/
ListSubscribedWorkteamsResult listSubscribedWorkteams(ListSubscribedWorkteamsRequest listSubscribedWorkteamsRequest);
/**
*
* 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
*
*
*
* @param listTrainingJobsRequest
* @return Result of the ListTrainingJobs operation returned by the service.
* @sample AmazonSageMaker.ListTrainingJobs
* @see AWS API
* Documentation
*/
ListTrainingJobsResult listTrainingJobs(ListTrainingJobsRequest listTrainingJobsRequest);
/**
*
* 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
*
*
*
*
* @param listTrialComponentsRequest
* @return Result of the ListTrialComponents operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.ListTrialComponents
* @see AWS
* API Documentation
*/
ListTrialComponentsResult listTrialComponents(ListTrialComponentsRequest listTrialComponentsRequest);
/**
*
* 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.
*
*
* @param listWorkteamsRequest
* @return Result of the ListWorkteams operation returned by the service.
* @sample AmazonSageMaker.ListWorkteams
* @see AWS API
* Documentation
*/
ListWorkteamsResult listWorkteams(ListWorkteamsRequest listWorkteamsRequest);
/**
*
* 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.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @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 an Experiment
* 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.
*
*
*
* @param searchRequest
* @return Result of the Search operation returned by the service.
* @sample AmazonSageMaker.Search
* @see AWS API
* Documentation
*/
SearchResult search(SearchRequest searchRequest);
/**
*
* 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.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @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.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @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 an Experiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.StartInferenceExperiment
* @see AWS API Documentation
*/
StartInferenceExperimentResult startInferenceExperiment(StartInferenceExperimentRequest startInferenceExperimentRequest);
/**
*
* Programmatically start an MLflow Tracking Server.
*
*
* @param startMlflowTrackingServerRequest
* @return Result of the StartMlflowTrackingServer 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 an Experiment
* or Artifact.
* @sample AmazonSageMaker.StartMlflowTrackingServer
* @see AWS API Documentation
*/
StartMlflowTrackingServerResult startMlflowTrackingServer(StartMlflowTrackingServerRequest startMlflowTrackingServerRequest);
/**
*
* Starts a previously stopped monitoring schedule.
*
*
*
* By default, when you successfully create a new schedule, the status of a monitoring schedule is
* scheduled.
*
*
*
* @param startMonitoringScheduleRequest
* @return Result of the StartMonitoringSchedule operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.StartMonitoringSchedule
* @see AWS API Documentation
*/
StartMonitoringScheduleResult startMonitoringSchedule(StartMonitoringScheduleRequest startMonitoringScheduleRequest);
/**
*
* 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.
*
*
* @param startNotebookInstanceRequest
* @return Result of the StartNotebookInstance 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.StartNotebookInstance
* @see AWS API Documentation
*/
StartNotebookInstanceResult startNotebookInstance(StartNotebookInstanceRequest startNotebookInstanceRequest);
/**
*
* 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.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @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.
*
*
* @param stopCompilationJobRequest
* @return Result of the StopCompilationJob operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.StopCompilationJob
* @see AWS
* API Documentation
*/
StopCompilationJobResult stopCompilationJob(StopCompilationJobRequest stopCompilationJobRequest);
/**
*
* 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.
*
*
* @param stopHyperParameterTuningJobRequest
* @return Result of the StopHyperParameterTuningJob operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.StopHyperParameterTuningJob
* @see AWS API Documentation
*/
StopHyperParameterTuningJobResult stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest stopHyperParameterTuningJobRequest);
/**
*
* 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 an Experiment
* 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);
/**
*
* Programmatically stop an MLflow Tracking Server.
*
*
* @param stopMlflowTrackingServerRequest
* @return Result of the StopMlflowTrackingServer 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 an Experiment
* or Artifact.
* @sample AmazonSageMaker.StopMlflowTrackingServer
* @see AWS API Documentation
*/
StopMlflowTrackingServerResult stopMlflowTrackingServer(StopMlflowTrackingServerRequest stopMlflowTrackingServerRequest);
/**
*
* 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.
*
*
* @param stopNotebookInstanceRequest
* @return Result of the StopNotebookInstance operation returned by the service.
* @sample AmazonSageMaker.StopNotebookInstance
* @see AWS
* API Documentation
*/
StopNotebookInstanceResult stopNotebookInstance(StopNotebookInstanceRequest stopNotebookInstanceRequest);
/**
*
* Ends a running inference optimization job.
*
*
* @param stopOptimizationJobRequest
* @return Result of the StopOptimizationJob operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.StopOptimizationJob
* @see AWS
* API Documentation
*/
StopOptimizationJobResult stopOptimizationJob(StopOptimizationJobRequest stopOptimizationJobRequest);
/**
*
* 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.
*
*
* @param stopPipelineExecutionRequest
* @return Result of the StopPipelineExecution 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 an Experiment
* or Artifact.
* @sample AmazonSageMaker.StopPipelineExecution
* @see AWS API Documentation
*/
StopPipelineExecutionResult stopPipelineExecution(StopPipelineExecutionRequest stopPipelineExecutionRequest);
/**
*
* 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.
*
*
* @param stopTrainingJobRequest
* @return Result of the StopTrainingJob operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.StopTrainingJob
* @see AWS API
* Documentation
*/
StopTrainingJobResult stopTrainingJob(StopTrainingJobRequest stopTrainingJobRequest);
/**
*
* 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.
*
*
* @param stopTransformJobRequest
* @return Result of the StopTransformJob operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.StopTransformJob
* @see AWS API
* Documentation
*/
StopTransformJobResult stopTransformJob(StopTransformJobRequest stopTransformJobRequest);
/**
*
* 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 an Experiment
* 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 an Experiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.UpdateArtifact
* @see AWS API
* Documentation
*/
UpdateArtifactResult updateArtifact(UpdateArtifactRequest updateArtifactRequest);
/**
*
* Updates a SageMaker HyperPod cluster.
*
*
* @param updateClusterRequest
* @return Result of the UpdateCluster 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.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @sample AmazonSageMaker.UpdateCluster
* @see AWS API
* Documentation
*/
UpdateClusterResult updateCluster(UpdateClusterRequest updateClusterRequest);
/**
*
* Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this
* API, see Update the SageMaker HyperPod platform software of a cluster.
*
*
* @param updateClusterSoftwareRequest
* @return Result of the UpdateClusterSoftware 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 an Experiment
* or Artifact.
* @sample AmazonSageMaker.UpdateClusterSoftware
* @see AWS API Documentation
*/
UpdateClusterSoftwareResult updateClusterSoftware(UpdateClusterSoftwareRequest updateClusterSoftwareRequest);
/**
*
* Updates the specified Git repository with the specified values.
*
*
* @param updateCodeRepositoryRequest
* @return Result of the UpdateCodeRepository operation returned by the service.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @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 an Experiment
* 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 EndpointConfig specified in the request to a new fleet of instances. SageMaker shifts
* endpoint traffic to the new instances with the updated endpoint configuration and then deletes the old instances
* using the previous EndpointConfig (there is no availability loss). For more information about how to
* control the update and traffic shifting process, see Update models in
* production.
*
*
* 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.
*
*
*
* @param updateEndpointRequest
* @return Result of the UpdateEndpoint 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.UpdateEndpoint
* @see AWS API
* Documentation
*/
UpdateEndpointResult updateEndpoint(UpdateEndpointRequest updateEndpointRequest);
/**
*
* 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.
*
*
* @param updateEndpointWeightsAndCapacitiesRequest
* @return Result of the UpdateEndpointWeightsAndCapacities 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.UpdateEndpointWeightsAndCapacities
* @see AWS API Documentation
*/
UpdateEndpointWeightsAndCapacitiesResult updateEndpointWeightsAndCapacities(
UpdateEndpointWeightsAndCapacitiesRequest updateEndpointWeightsAndCapacitiesRequest);
/**
*
* 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 an Experiment
* 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. To remove the default TtlDuration from an existing feature
* group, use the UpdateFeatureGroup API and set the TtlDuration Unit and
* Value to null.
*
*
* @param updateFeatureGroupRequest
* @return Result of the UpdateFeatureGroup 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.UpdateFeatureGroup
* @see AWS
* API Documentation
*/
UpdateFeatureGroupResult updateFeatureGroup(UpdateFeatureGroupRequest updateFeatureGroupRequest);
/**
*
* 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.
*
*
* @param updateHubRequest
* @return Result of the UpdateHub operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.UpdateHub
* @see AWS API
* Documentation
*/
UpdateHubResult updateHub(UpdateHubRequest updateHubRequest);
/**
*
* 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 component.
*
*
* @param updateInferenceComponentRequest
* @return Result of the UpdateInferenceComponent 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.UpdateInferenceComponent
* @see AWS API Documentation
*/
UpdateInferenceComponentResult updateInferenceComponent(UpdateInferenceComponentRequest updateInferenceComponentRequest);
/**
*
* Runtime settings for a model that is deployed with an inference component.
*
*
* @param updateInferenceComponentRuntimeConfigRequest
* @return Result of the UpdateInferenceComponentRuntimeConfig 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.UpdateInferenceComponentRuntimeConfig
* @see AWS API Documentation
*/
UpdateInferenceComponentRuntimeConfigResult updateInferenceComponentRuntimeConfig(
UpdateInferenceComponentRuntimeConfigRequest updateInferenceComponentRuntimeConfigRequest);
/**
*
* 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.
*
*
* @param updateInferenceExperimentRequest
* @return Result of the UpdateInferenceExperiment operation returned by the service.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.UpdateInferenceExperiment
* @see AWS API Documentation
*/
UpdateInferenceExperimentResult updateInferenceExperiment(UpdateInferenceExperimentRequest updateInferenceExperimentRequest);
/**
*
* Updates properties of an existing MLflow Tracking Server.
*
*
* @param updateMlflowTrackingServerRequest
* @return Result of the UpdateMlflowTrackingServer 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 an Experiment
* or Artifact.
* @sample AmazonSageMaker.UpdateMlflowTrackingServer
* @see AWS API Documentation
*/
UpdateMlflowTrackingServerResult updateMlflowTrackingServer(UpdateMlflowTrackingServerRequest updateMlflowTrackingServerRequest);
/**
*
* Update an Amazon SageMaker Model Card.
*
*
*
* You cannot update both model card content and model card status in a single call.
*
*
*
* @param updateModelCardRequest
* @return Result of the UpdateModelCard 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 an 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.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @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.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @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.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @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.
*
*
*
* @param updateProjectRequest
* @return Result of the UpdateProject operation returned by the service.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
* or Artifact.
* @sample AmazonSageMaker.UpdateProject
* @see AWS API
* Documentation
*/
UpdateProjectResult updateProject(UpdateProjectRequest updateProjectRequest);
/**
*
* 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.
* @throws ResourceLimitExceededException
* You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
* created.
* @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 an Experiment
* 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 an Experiment
* 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.
*
*
*
* @param updateWorkforceRequest
* @return Result of the UpdateWorkforce operation returned by the service.
* @throws ConflictException
* There was a conflict when you attempted to modify a SageMaker entity such as an 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();
}