@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class AbstractAmazonSageMaker extends Object implements AmazonSageMaker
AmazonSageMaker. Convenient method forms pass through to the corresponding
 overload that takes a request object, which throws an UnsupportedOperationException.ENDPOINT_PREFIXpublic AddAssociationResult addAssociation(AddAssociationRequest request)
AmazonSageMakerCreates 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.
addAssociation in interface AmazonSageMakerpublic AddTagsResult addTags(AddTagsRequest request)
AmazonSageMakerAdds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see AWS Tagging Strategies.
 Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the
 hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter
 tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter
 tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you
 first create the tuning job by specifying them in the Tags parameter of
 CreateHyperParameterTuningJob
 
addTags in interface AmazonSageMakerpublic AssociateTrialComponentResult associateTrialComponent(AssociateTrialComponentRequest request)
AmazonSageMakerAssociates 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.
associateTrialComponent in interface AmazonSageMakerpublic CreateActionResult createAction(CreateActionRequest request)
AmazonSageMakerCreates 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.
createAction in interface AmazonSageMakerpublic CreateAlgorithmResult createAlgorithm(CreateAlgorithmRequest request)
AmazonSageMakerCreate a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
createAlgorithm in interface AmazonSageMakerpublic CreateAppResult createApp(CreateAppRequest request)
AmazonSageMaker
 Creates a running app for the specified UserProfile. Supported apps are JupyterServer and
 KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio upon access to the
 associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps
 active simultaneously.
 
createApp in interface AmazonSageMakerpublic CreateAppImageConfigResult createAppImageConfig(CreateAppImageConfigRequest request)
AmazonSageMakerCreates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.
createAppImageConfig in interface AmazonSageMakerpublic CreateArtifactResult createArtifact(CreateArtifactRequest request)
AmazonSageMakerCreates 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.
createArtifact in interface AmazonSageMakerpublic CreateAutoMLJobResult createAutoMLJob(CreateAutoMLJobRequest request)
AmazonSageMakerCreates an Autopilot job.
Find the best performing model after you run an Autopilot job by calling .
For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
createAutoMLJob in interface AmazonSageMakerpublic CreateCodeRepositoryResult createCodeRepository(CreateCodeRepositoryRequest request)
AmazonSageMakerCreates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in AWS CodeCommit or in any other Git repository.
createCodeRepository in interface AmazonSageMakerpublic CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest request)
AmazonSageMakerStarts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with AWS IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
 You can also provide a Tag to track the model compilation job's resource use and costs. The response
 body contains the CompilationJobArn for the compiled job.
 
To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
createCompilationJob in interface AmazonSageMakerpublic CreateContextResult createContext(CreateContextRequest request)
AmazonSageMakerCreates 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.
createContext in interface AmazonSageMakerpublic CreateDataQualityJobDefinitionResult createDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest request)
AmazonSageMakerCreates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
createDataQualityJobDefinition in interface AmazonSageMakerpublic CreateDeviceFleetResult createDeviceFleet(CreateDeviceFleetRequest request)
AmazonSageMakerCreates a device fleet.
createDeviceFleet in interface AmazonSageMakerpublic CreateDomainResult createDomain(CreateDomainRequest request)
AmazonSageMaker
 Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic
 File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon
 Virtual Private Cloud (VPC) configurations. An AWS account is limited to one domain per region. Users within a
 domain can share notebook files and other artifacts with each other.
 
EFS storage
When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
SageMaker uses the AWS Key Management Service (AWS KMS) to encrypt the EFS volume attached to the domain with an AWS managed customer master key (CMK) by default. For more control, you can specify a customer managed CMK. For more information, see Protect Data at Rest Using Encryption.
VPC configuration
 All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For
 other Studio traffic, you can specify the AppNetworkAccessType parameter.
 AppNetworkAccessType corresponds to the network access type that you choose when you onboard to
 Studio. The following options are available:
 
 PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows
 internet access. This is the default value.
 
 VpcOnly - All Studio traffic is through the specified VPC and subnets. Internet access is disabled
 by default. To allow internet access, you must specify a NAT gateway.
 
When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.
For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
createDomain in interface AmazonSageMakerpublic CreateEdgePackagingJobResult createEdgePackagingJob(CreateEdgePackagingJobRequest request)
AmazonSageMakerStarts 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.
createEdgePackagingJob in interface AmazonSageMakerpublic CreateEndpointResult createEndpoint(CreateEndpointRequest request)
AmazonSageMakerCreates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API to deploy models using Amazon SageMaker hosting services.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
 You must not delete an EndpointConfig that is in use by an endpoint that is live or while the
 UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To
 update an endpoint, you must create a new EndpointConfig.
 
The endpoint name must be unique within an AWS Region in your AWS account.
When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
 When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
 exists. When you read data from a DynamoDB table supporting 
 Eventually Consistent Reads , the response might not reflect the results of a recently completed
 write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
 this causes a validation error. If you repeat your read request after a short time, the response should return
 the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
 call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
 DynamoDB eventually consistent read.
 
 When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it
 creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming
 requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
 
If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.
 Option 1: For a full Amazon SageMaker access, search and attach the AmazonSageMakerFullAccess
 policy.
 
Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:
 "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]
 
 "Resource": [
 
 "arn:aws:sagemaker:region:account-id:endpoint/endpointName"
 
 "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
 
 ]
 
For more information, see Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference.
createEndpoint in interface AmazonSageMakerpublic CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest request)
AmazonSageMaker
 Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the
 configuration, you identify one or more models, created using the CreateModel API, to deploy and the
 resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.
 
Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.
 In the request, you define a ProductionVariant, for each model that you want to deploy. Each
 ProductionVariant parameter also describes the resources that you want Amazon SageMaker to
 provision. This includes the number and type of ML compute instances to deploy.
 
 If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you
 want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign
 traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model
 A, and one-third to model B.
 
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
 When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
 exists. When you read data from a DynamoDB table supporting 
 Eventually Consistent Reads , the response might not reflect the results of a recently completed
 write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
 this causes a validation error. If you repeat your read request after a short time, the response should return
 the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
 call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
 DynamoDB eventually consistent read.
 
createEndpointConfig in interface AmazonSageMakerpublic CreateExperimentResult createExperiment(CreateExperimentRequest request)
AmazonSageMakerCreates an SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.
The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
 To add a description to an experiment, specify the optional Description parameter. To add a
 description later, or to change the description, call the UpdateExperiment API.
 
To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
createExperiment in interface AmazonSageMakerpublic CreateFeatureGroupResult createFeatureGroup(CreateFeatureGroupRequest request)
AmazonSageMaker
 Create a new FeatureGroup. A FeatureGroup is a group of Features defined
 in the FeatureStore to describe a Record.
 
 The FeatureGroup defines the schema and features contained in the FeatureGroup. A
 FeatureGroup definition is composed of a list of Features, a
 RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its
 OnlineStore and OfflineStore. Check AWS service quotas to see the
 FeatureGroups quota for your AWS account.
 
 You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a
 FeatureGroup.
 
createFeatureGroup in interface AmazonSageMakerpublic CreateFlowDefinitionResult createFlowDefinition(CreateFlowDefinitionRequest request)
AmazonSageMakerCreates a flow definition.
createFlowDefinition in interface AmazonSageMakerpublic CreateHumanTaskUiResult createHumanTaskUi(CreateHumanTaskUiRequest request)
AmazonSageMakerDefines 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.
createHumanTaskUi in interface AmazonSageMakerpublic CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request)
AmazonSageMakerStarts 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.
createHyperParameterTuningJob in interface AmazonSageMakerpublic CreateImageResult createImage(CreateImageRequest request)
AmazonSageMakerCreates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image.
createImage in interface AmazonSageMakerpublic CreateImageVersionResult createImageVersion(CreateImageVersionRequest request)
AmazonSageMaker
 Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon
 Container Registry (ECR) container image specified by BaseImage.
 
createImageVersion in interface AmazonSageMakerpublic CreateLabelingJobResult createLabelingJob(CreateLabelingJobRequest request)
AmazonSageMakerCreates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
You can select your workforce from one of three providers:
A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas.
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
 You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job
 stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled.
 A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send
 new data objects to an active (InProgress) streaming labeling job in real time. To learn how to
 create a static labeling job, see Create a Labeling Job
 (API)  in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling
 Job.
 
createLabelingJob in interface AmazonSageMakerpublic CreateModelResult createModel(CreateModelRequest request)
AmazonSageMakerCreates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.
 To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then
 create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers
 that you defined for the model in the hosting environment.
 
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
 To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon
 SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
 
 In the CreateModel request, you must define a container with the PrimaryContainer
 parameter.
 
In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.
createModel in interface AmazonSageMakerpublic CreateModelBiasJobDefinitionResult createModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest request)
AmazonSageMakerCreates the definition for a model bias job.
createModelBiasJobDefinition in interface AmazonSageMakerpublic CreateModelExplainabilityJobDefinitionResult createModelExplainabilityJobDefinition(CreateModelExplainabilityJobDefinitionRequest request)
AmazonSageMakerCreates the definition for a model explainability job.
createModelExplainabilityJobDefinition in interface AmazonSageMakerpublic CreateModelPackageResult createModelPackage(CreateModelPackageRequest request)
AmazonSageMakerCreates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
 To create a model package by specifying a Docker container that contains your inference code and the Amazon S3
 location of your model artifacts, provide values for InferenceSpecification. To create a model from
 an algorithm resource that you created or subscribed to in AWS Marketplace, provide a value for
 SourceAlgorithmSpecification.
 
There are two types of model packages:
Versioned - a model that is part of a model group in the model registry.
Unversioned - a model package that is not part of a model group.
createModelPackage in interface AmazonSageMakerpublic CreateModelPackageGroupResult createModelPackageGroup(CreateModelPackageGroupRequest request)
AmazonSageMakerCreates a model group. A model group contains a group of model versions.
createModelPackageGroup in interface AmazonSageMakerpublic CreateModelQualityJobDefinitionResult createModelQualityJobDefinition(CreateModelQualityJobDefinitionRequest request)
AmazonSageMakerCreates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
createModelQualityJobDefinition in interface AmazonSageMakerpublic CreateMonitoringScheduleResult createMonitoringSchedule(CreateMonitoringScheduleRequest request)
AmazonSageMakerCreates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
createMonitoringSchedule in interface AmazonSageMakerpublic CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest request)
AmazonSageMakerCreates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
 In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run.
 Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model
 training, and attaches an ML storage volume to the notebook instance.
 
Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, Amazon SageMaker does the following:
Creates a network interface in the Amazon SageMaker VPC.
 (Option) If you specified SubnetId, Amazon SageMaker creates a network interface in your own VPC,
 which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon
 SageMaker attaches the security group that you specified in the request to the network interface that it creates
 in your VPC.
 
 Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified
 SubnetId of your VPC, Amazon SageMaker specifies both network interfaces when launching this
 instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security
 groups allow it.
 
After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
createNotebookInstance in interface AmazonSageMakerpublic CreateNotebookInstanceLifecycleConfigResult createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest request)
AmazonSageMakerCreates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.
Each lifecycle configuration script has a limit of 16384 characters.
 The value of the $PATH environment variable that is available to both scripts is
 /sbin:bin:/usr/sbin:/usr/bin.
 
 View CloudWatch Logs for notebook instance lifecycle configurations in log group
 /aws/sagemaker/NotebookInstances in log stream
 [notebook-instance-name]/[LifecycleConfigHook].
 
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
createNotebookInstanceLifecycleConfig in interface AmazonSageMakerpublic CreatePipelineResult createPipeline(CreatePipelineRequest request)
AmazonSageMakerCreates a pipeline using a JSON pipeline definition.
createPipeline in interface AmazonSageMakerpublic CreatePresignedDomainUrlResult createPresignedDomainUrl(CreatePresignedDomainUrlRequest request)
AmazonSageMakerCreates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
 The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You
 can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit
 expires, you are directed to the AWS console sign-in page.
 
createPresignedDomainUrl in interface AmazonSageMakerpublic CreatePresignedNotebookInstanceUrlResult createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest request)
AmazonSageMaker
 Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker
 console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing
 the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the
 page.
 
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
 You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify.
 Use the NotIpAddress condition operator and the aws:SourceIP condition context key to
 specify the list of IP addresses that you want to have access to the notebook instance. For more information, see
 Limit Access to a Notebook Instance by IP Address.
 
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
createPresignedNotebookInstanceUrl in interface AmazonSageMakerpublic CreateProcessingJobResult createProcessingJob(CreateProcessingJobRequest request)
AmazonSageMakerCreates a processing job.
createProcessingJob in interface AmazonSageMakerpublic CreateProjectResult createProject(CreateProjectRequest request)
AmazonSageMakerCreates 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.
createProject in interface AmazonSageMakerpublic CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest request)
AmazonSageMakerStarts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker, provided that you know how to use them for inference.
In the request body, you provide the following:
 AlgorithmSpecification - Identifies the training algorithm to use.
 
 HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model
 parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of
 hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
 
 InputDataConfig - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is
 stored.
 
 OutputDataConfig - Identifies the Amazon S3 bucket where you want Amazon SageMaker to save the
 results of model training.
 
 ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy
 for model training. In distributed training, you specify more than one instance.
 
 EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by
 using Amazon EC2 Spot instances. For more information, see Managed Spot
 Training.
 
 RoleArn - The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your
 behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can
 successfully complete model training.
 
 StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time
 limit for training. Use MaxWaitTimeInSeconds to specify how long you are willing to wait for a
 managed spot training job to complete.
 
 Environment - The environment variables to set in the Docker container.
 
For more information about Amazon SageMaker, see How It Works.
createTrainingJob in interface AmazonSageMakerpublic CreateTransformJobResult createTransformJob(CreateTransformJobRequest request)
AmazonSageMakerStarts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
To perform batch transformations, you create a transform job and use the data that you have readily available.
In the request body, you provide the following:
 TransformJobName - Identifies the transform job. The name must be unique within an AWS Region in an
 AWS account.
 
 ModelName - Identifies the model to use. ModelName must be the name of an existing
 Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see
 CreateModel.
 
 TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is
 stored.
 
 TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the
 results from the transform job.
 
 TransformResources - Identifies the ML compute instances for the transform job.
 
For more information about how batch transformation works, see Batch Transform.
createTransformJob in interface AmazonSageMakerpublic CreateTrialResult createTrial(CreateTrialRequest request)
AmazonSageMakerCreates an Amazon SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single Amazon SageMaker experiment.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
createTrial in interface AmazonSageMakerpublic CreateTrialComponentResult createTrialComponent(CreateTrialComponentRequest request)
AmazonSageMakerCreates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
 CreateTrialComponent can only be invoked from within an Amazon SageMaker managed environment. This
 includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call
 to CreateTrialComponent from outside one of these environments results in an error.
 
createTrialComponent in interface AmazonSageMakerpublic CreateUserProfileResult createUserProfile(CreateUserProfileRequest request)
AmazonSageMakerCreates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
createUserProfile in interface AmazonSageMakerpublic CreateWorkforceResult createWorkforce(CreateWorkforceRequest request)
AmazonSageMakerUse this operation to create a workforce. This operation will return an error if a workforce already exists in the AWS Region that you specify. You can only create one workforce in each AWS Region per AWS account.
 If you want to create a new workforce in an AWS Region where a workforce already exists, use the API operation to
 delete the existing workforce and then use CreateWorkforce to create a new workforce.
 
 To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in
 CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console.
 For more information, see  Create a Private
 Workforce (Amazon Cognito).
 
 To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in
 OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and
 Amazon A2I to create work teams. For more information, see  Create a Private
 Workforce (OIDC IdP).
 
createWorkforce in interface AmazonSageMakerpublic CreateWorkteamResult createWorkteam(CreateWorkteamRequest request)
AmazonSageMakerCreates 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.
createWorkteam in interface AmazonSageMakerpublic DeleteActionResult deleteAction(DeleteActionRequest request)
AmazonSageMakerDeletes an action.
deleteAction in interface AmazonSageMakerpublic DeleteAlgorithmResult deleteAlgorithm(DeleteAlgorithmRequest request)
AmazonSageMakerRemoves the specified algorithm from your account.
deleteAlgorithm in interface AmazonSageMakerpublic DeleteAppResult deleteApp(DeleteAppRequest request)
AmazonSageMakerUsed to stop and delete an app.
deleteApp in interface AmazonSageMakerpublic DeleteAppImageConfigResult deleteAppImageConfig(DeleteAppImageConfigRequest request)
AmazonSageMakerDeletes an AppImageConfig.
deleteAppImageConfig in interface AmazonSageMakerpublic DeleteArtifactResult deleteArtifact(DeleteArtifactRequest request)
AmazonSageMaker
 Deletes an artifact. Either ArtifactArn or Source must be specified.
 
deleteArtifact in interface AmazonSageMakerpublic DeleteAssociationResult deleteAssociation(DeleteAssociationRequest request)
AmazonSageMakerDeletes an association.
deleteAssociation in interface AmazonSageMakerpublic DeleteCodeRepositoryResult deleteCodeRepository(DeleteCodeRepositoryRequest request)
AmazonSageMakerDeletes the specified Git repository from your account.
deleteCodeRepository in interface AmazonSageMakerpublic DeleteContextResult deleteContext(DeleteContextRequest request)
AmazonSageMakerDeletes an context.
deleteContext in interface AmazonSageMakerpublic DeleteDataQualityJobDefinitionResult deleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest request)
AmazonSageMakerDeletes a data quality monitoring job definition.
deleteDataQualityJobDefinition in interface AmazonSageMakerpublic DeleteDeviceFleetResult deleteDeviceFleet(DeleteDeviceFleetRequest request)
AmazonSageMakerDeletes a fleet.
deleteDeviceFleet in interface AmazonSageMakerpublic DeleteDomainResult deleteDomain(DeleteDomainRequest request)
AmazonSageMakerUsed to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
deleteDomain in interface AmazonSageMakerpublic DeleteEndpointResult deleteEndpoint(DeleteEndpointRequest request)
AmazonSageMakerDeletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.
Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
deleteEndpoint in interface AmazonSageMakerpublic DeleteEndpointConfigResult deleteEndpointConfig(DeleteEndpointConfigRequest request)
AmazonSageMaker
 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.
 
deleteEndpointConfig in interface AmazonSageMakerpublic DeleteExperimentResult deleteExperiment(DeleteExperimentRequest request)
AmazonSageMakerDeletes an Amazon SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
deleteExperiment in interface AmazonSageMakerpublic DeleteFeatureGroupResult deleteFeatureGroup(DeleteFeatureGroupRequest request)
AmazonSageMaker
 Delete the FeatureGroup and any data that was written to the OnlineStore of the
 FeatureGroup. Data cannot be accessed from the OnlineStore immediately after
 DeleteFeatureGroup is called.
 
 Data written into the OfflineStore will not be deleted. The AWS Glue database and tables that are
 automatically created for your OfflineStore are not deleted.
 
deleteFeatureGroup in interface AmazonSageMakerpublic DeleteFlowDefinitionResult deleteFlowDefinition(DeleteFlowDefinitionRequest request)
AmazonSageMakerDeletes the specified flow definition.
deleteFlowDefinition in interface AmazonSageMakerpublic DeleteHumanTaskUiResult deleteHumanTaskUi(DeleteHumanTaskUiRequest request)
AmazonSageMakerUse this operation to delete a human task user interface (worker task template).
 To see a list of human task user interfaces (work task templates) in your account, use . When you delete a worker
 task template, it no longer appears when you call ListHumanTaskUis.
 
deleteHumanTaskUi in interface AmazonSageMakerpublic DeleteImageResult deleteImage(DeleteImageRequest request)
AmazonSageMakerDeletes a SageMaker image and all versions of the image. The container images aren't deleted.
deleteImage in interface AmazonSageMakerpublic DeleteImageVersionResult deleteImageVersion(DeleteImageVersionRequest request)
AmazonSageMakerDeletes a version of a SageMaker image. The container image the version represents isn't deleted.
deleteImageVersion in interface AmazonSageMakerpublic DeleteModelResult deleteModel(DeleteModelRequest request)
AmazonSageMaker
 Deletes a model. The DeleteModel API deletes only the model entry that was created in Amazon
 SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the
 IAM role that you specified when creating the model.
 
deleteModel in interface AmazonSageMakerpublic DeleteModelBiasJobDefinitionResult deleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest request)
AmazonSageMakerDeletes an Amazon SageMaker model bias job definition.
deleteModelBiasJobDefinition in interface AmazonSageMakerpublic DeleteModelExplainabilityJobDefinitionResult deleteModelExplainabilityJobDefinition(DeleteModelExplainabilityJobDefinitionRequest request)
AmazonSageMakerDeletes an Amazon SageMaker model explainability job definition.
deleteModelExplainabilityJobDefinition in interface AmazonSageMakerpublic DeleteModelPackageResult deleteModelPackage(DeleteModelPackageRequest request)
AmazonSageMakerDeletes a model package.
A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
deleteModelPackage in interface AmazonSageMakerpublic DeleteModelPackageGroupResult deleteModelPackageGroup(DeleteModelPackageGroupRequest request)
AmazonSageMakerDeletes the specified model group.
deleteModelPackageGroup in interface AmazonSageMakerpublic DeleteModelPackageGroupPolicyResult deleteModelPackageGroupPolicy(DeleteModelPackageGroupPolicyRequest request)
AmazonSageMakerDeletes a model group resource policy.
deleteModelPackageGroupPolicy in interface AmazonSageMakerpublic DeleteModelQualityJobDefinitionResult deleteModelQualityJobDefinition(DeleteModelQualityJobDefinitionRequest request)
AmazonSageMakerDeletes the secified model quality monitoring job definition.
deleteModelQualityJobDefinition in interface AmazonSageMakerpublic DeleteMonitoringScheduleResult deleteMonitoringSchedule(DeleteMonitoringScheduleRequest request)
AmazonSageMakerDeletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
deleteMonitoringSchedule in interface AmazonSageMakerpublic DeleteNotebookInstanceResult deleteNotebookInstance(DeleteNotebookInstanceRequest request)
AmazonSageMaker
 Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the
 StopNotebookInstance API.
 
When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
deleteNotebookInstance in interface AmazonSageMakerpublic DeleteNotebookInstanceLifecycleConfigResult deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest request)
AmazonSageMakerDeletes a notebook instance lifecycle configuration.
deleteNotebookInstanceLifecycleConfig in interface AmazonSageMakerpublic DeletePipelineResult deletePipeline(DeletePipelineRequest request)
AmazonSageMakerDeletes a pipeline if there are no in-progress executions.
deletePipeline in interface AmazonSageMakerpublic DeleteProjectResult deleteProject(DeleteProjectRequest request)
AmazonSageMakerDelete the specified project.
deleteProject in interface AmazonSageMakerpublic DeleteTagsResult deleteTags(DeleteTagsRequest request)
AmazonSageMakerDeletes the specified tags from an Amazon SageMaker resource.
 To list a resource's tags, use the ListTags API.
 
When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.
deleteTags in interface AmazonSageMakerpublic DeleteTrialResult deleteTrial(DeleteTrialRequest request)
AmazonSageMakerDeletes 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.
deleteTrial in interface AmazonSageMakerpublic DeleteTrialComponentResult deleteTrialComponent(DeleteTrialComponentRequest request)
AmazonSageMakerDeletes 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.
deleteTrialComponent in interface AmazonSageMakerpublic DeleteUserProfileResult deleteUserProfile(DeleteUserProfileRequest request)
AmazonSageMakerDeletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
deleteUserProfile in interface AmazonSageMakerpublic DeleteWorkforceResult deleteWorkforce(DeleteWorkforceRequest request)
AmazonSageMakerUse this operation to delete a workforce.
If you want to create a new workforce in an AWS Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.
 If a private workforce contains one or more work teams, you must use the operation to delete all work teams
 before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will
 recieve a ResourceInUse error.
 
deleteWorkforce in interface AmazonSageMakerpublic DeleteWorkteamResult deleteWorkteam(DeleteWorkteamRequest request)
AmazonSageMakerDeletes an existing work team. This operation can't be undone.
deleteWorkteam in interface AmazonSageMakerpublic DeregisterDevicesResult deregisterDevices(DeregisterDevicesRequest request)
AmazonSageMakerDeregisters the specified devices. After you deregister a device, you will need to re-register the devices.
deregisterDevices in interface AmazonSageMakerpublic DescribeActionResult describeAction(DescribeActionRequest request)
AmazonSageMakerDescribes an action.
describeAction in interface AmazonSageMakerpublic DescribeAlgorithmResult describeAlgorithm(DescribeAlgorithmRequest request)
AmazonSageMakerReturns a description of the specified algorithm that is in your account.
describeAlgorithm in interface AmazonSageMakerpublic DescribeAppResult describeApp(DescribeAppRequest request)
AmazonSageMakerDescribes the app.
describeApp in interface AmazonSageMakerpublic DescribeAppImageConfigResult describeAppImageConfig(DescribeAppImageConfigRequest request)
AmazonSageMakerDescribes an AppImageConfig.
describeAppImageConfig in interface AmazonSageMakerpublic DescribeArtifactResult describeArtifact(DescribeArtifactRequest request)
AmazonSageMakerDescribes an artifact.
describeArtifact in interface AmazonSageMakerpublic DescribeAutoMLJobResult describeAutoMLJob(DescribeAutoMLJobRequest request)
AmazonSageMakerReturns information about an Amazon SageMaker AutoML job.
describeAutoMLJob in interface AmazonSageMakerpublic DescribeCodeRepositoryResult describeCodeRepository(DescribeCodeRepositoryRequest request)
AmazonSageMakerGets details about the specified Git repository.
describeCodeRepository in interface AmazonSageMakerpublic DescribeCompilationJobResult describeCompilationJob(DescribeCompilationJobRequest request)
AmazonSageMakerReturns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
describeCompilationJob in interface AmazonSageMakerpublic DescribeContextResult describeContext(DescribeContextRequest request)
AmazonSageMakerDescribes a context.
describeContext in interface AmazonSageMakerpublic DescribeDataQualityJobDefinitionResult describeDataQualityJobDefinition(DescribeDataQualityJobDefinitionRequest request)
AmazonSageMakerGets the details of a data quality monitoring job definition.
describeDataQualityJobDefinition in interface AmazonSageMakerpublic DescribeDeviceResult describeDevice(DescribeDeviceRequest request)
AmazonSageMakerDescribes the device.
describeDevice in interface AmazonSageMakerpublic DescribeDeviceFleetResult describeDeviceFleet(DescribeDeviceFleetRequest request)
AmazonSageMakerA description of the fleet the device belongs to.
describeDeviceFleet in interface AmazonSageMakerpublic DescribeDomainResult describeDomain(DescribeDomainRequest request)
AmazonSageMakerThe description of the domain.
describeDomain in interface AmazonSageMakerpublic DescribeEdgePackagingJobResult describeEdgePackagingJob(DescribeEdgePackagingJobRequest request)
AmazonSageMakerA description of edge packaging jobs.
describeEdgePackagingJob in interface AmazonSageMakerpublic DescribeEndpointResult describeEndpoint(DescribeEndpointRequest request)
AmazonSageMakerReturns the description of an endpoint.
describeEndpoint in interface AmazonSageMakerpublic DescribeEndpointConfigResult describeEndpointConfig(DescribeEndpointConfigRequest request)
AmazonSageMaker
 Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
 
describeEndpointConfig in interface AmazonSageMakerpublic DescribeExperimentResult describeExperiment(DescribeExperimentRequest request)
AmazonSageMakerProvides a list of an experiment's properties.
describeExperiment in interface AmazonSageMakerpublic DescribeFeatureGroupResult describeFeatureGroup(DescribeFeatureGroupRequest request)
AmazonSageMaker
 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.
 
describeFeatureGroup in interface AmazonSageMakerpublic DescribeFlowDefinitionResult describeFlowDefinition(DescribeFlowDefinitionRequest request)
AmazonSageMakerReturns information about the specified flow definition.
describeFlowDefinition in interface AmazonSageMakerpublic DescribeHumanTaskUiResult describeHumanTaskUi(DescribeHumanTaskUiRequest request)
AmazonSageMakerReturns information about the requested human task user interface (worker task template).
describeHumanTaskUi in interface AmazonSageMakerpublic DescribeHyperParameterTuningJobResult describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest request)
AmazonSageMakerGets a description of a hyperparameter tuning job.
describeHyperParameterTuningJob in interface AmazonSageMakerpublic DescribeImageResult describeImage(DescribeImageRequest request)
AmazonSageMakerDescribes a SageMaker image.
describeImage in interface AmazonSageMakerpublic DescribeImageVersionResult describeImageVersion(DescribeImageVersionRequest request)
AmazonSageMakerDescribes a version of a SageMaker image.
describeImageVersion in interface AmazonSageMakerpublic DescribeLabelingJobResult describeLabelingJob(DescribeLabelingJobRequest request)
AmazonSageMakerGets information about a labeling job.
describeLabelingJob in interface AmazonSageMakerpublic DescribeModelResult describeModel(DescribeModelRequest request)
AmazonSageMaker
 Describes a model that you created using the CreateModel API.
 
describeModel in interface AmazonSageMakerpublic DescribeModelBiasJobDefinitionResult describeModelBiasJobDefinition(DescribeModelBiasJobDefinitionRequest request)
AmazonSageMakerReturns a description of a model bias job definition.
describeModelBiasJobDefinition in interface AmazonSageMakerpublic DescribeModelExplainabilityJobDefinitionResult describeModelExplainabilityJobDefinition(DescribeModelExplainabilityJobDefinitionRequest request)
AmazonSageMakerReturns a description of a model explainability job definition.
describeModelExplainabilityJobDefinition in interface AmazonSageMakerpublic DescribeModelPackageResult describeModelPackage(DescribeModelPackageRequest request)
AmazonSageMakerReturns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.
To create models in Amazon SageMaker, buyers can subscribe to model packages listed on AWS Marketplace.
describeModelPackage in interface AmazonSageMakerpublic DescribeModelPackageGroupResult describeModelPackageGroup(DescribeModelPackageGroupRequest request)
AmazonSageMakerGets a description for the specified model group.
describeModelPackageGroup in interface AmazonSageMakerpublic DescribeModelQualityJobDefinitionResult describeModelQualityJobDefinition(DescribeModelQualityJobDefinitionRequest request)
AmazonSageMakerReturns a description of a model quality job definition.
describeModelQualityJobDefinition in interface AmazonSageMakerpublic DescribeMonitoringScheduleResult describeMonitoringSchedule(DescribeMonitoringScheduleRequest request)
AmazonSageMakerDescribes the schedule for a monitoring job.
describeMonitoringSchedule in interface AmazonSageMakerpublic DescribeNotebookInstanceResult describeNotebookInstance(DescribeNotebookInstanceRequest request)
AmazonSageMakerReturns information about a notebook instance.
describeNotebookInstance in interface AmazonSageMakerpublic DescribeNotebookInstanceLifecycleConfigResult describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest request)
AmazonSageMakerReturns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
describeNotebookInstanceLifecycleConfig in interface AmazonSageMakerpublic DescribePipelineResult describePipeline(DescribePipelineRequest request)
AmazonSageMakerDescribes the details of a pipeline.
describePipeline in interface AmazonSageMakerpublic DescribePipelineDefinitionForExecutionResult describePipelineDefinitionForExecution(DescribePipelineDefinitionForExecutionRequest request)
AmazonSageMakerDescribes the details of an execution's pipeline definition.
describePipelineDefinitionForExecution in interface AmazonSageMakerpublic DescribePipelineExecutionResult describePipelineExecution(DescribePipelineExecutionRequest request)
AmazonSageMakerDescribes the details of a pipeline execution.
describePipelineExecution in interface AmazonSageMakerpublic DescribeProcessingJobResult describeProcessingJob(DescribeProcessingJobRequest request)
AmazonSageMakerReturns a description of a processing job.
describeProcessingJob in interface AmazonSageMakerpublic DescribeProjectResult describeProject(DescribeProjectRequest request)
AmazonSageMakerDescribes the details of a project.
describeProject in interface AmazonSageMakerpublic DescribeSubscribedWorkteamResult describeSubscribedWorkteam(DescribeSubscribedWorkteamRequest request)
AmazonSageMakerGets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the AWS Marketplace.
describeSubscribedWorkteam in interface AmazonSageMakerpublic DescribeTrainingJobResult describeTrainingJob(DescribeTrainingJobRequest request)
AmazonSageMakerReturns 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.
 
describeTrainingJob in interface AmazonSageMakerpublic DescribeTransformJobResult describeTransformJob(DescribeTransformJobRequest request)
AmazonSageMakerReturns information about a transform job.
describeTransformJob in interface AmazonSageMakerpublic DescribeTrialResult describeTrial(DescribeTrialRequest request)
AmazonSageMakerProvides a list of a trial's properties.
describeTrial in interface AmazonSageMakerpublic DescribeTrialComponentResult describeTrialComponent(DescribeTrialComponentRequest request)
AmazonSageMakerProvides a list of a trials component's properties.
describeTrialComponent in interface AmazonSageMakerpublic DescribeUserProfileResult describeUserProfile(DescribeUserProfileRequest request)
AmazonSageMaker
 Describes a user profile. For more information, see CreateUserProfile.
 
describeUserProfile in interface AmazonSageMakerpublic DescribeWorkforceResult describeWorkforce(DescribeWorkforceRequest request)
AmazonSageMakerLists 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.
describeWorkforce in interface AmazonSageMakerpublic DescribeWorkteamResult describeWorkteam(DescribeWorkteamRequest request)
AmazonSageMakerGets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
describeWorkteam in interface AmazonSageMakerpublic DisableSagemakerServicecatalogPortfolioResult disableSagemakerServicecatalogPortfolio(DisableSagemakerServicecatalogPortfolioRequest request)
AmazonSageMakerDisables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
disableSagemakerServicecatalogPortfolio in interface AmazonSageMakerpublic DisassociateTrialComponentResult disassociateTrialComponent(DisassociateTrialComponentRequest request)
AmazonSageMakerDisassociates 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.
 
disassociateTrialComponent in interface AmazonSageMakerpublic EnableSagemakerServicecatalogPortfolioResult enableSagemakerServicecatalogPortfolio(EnableSagemakerServicecatalogPortfolioRequest request)
AmazonSageMakerEnables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
enableSagemakerServicecatalogPortfolio in interface AmazonSageMakerpublic GetDeviceFleetReportResult getDeviceFleetReport(GetDeviceFleetReportRequest request)
AmazonSageMakerDescribes a fleet.
getDeviceFleetReport in interface AmazonSageMakerpublic GetModelPackageGroupPolicyResult getModelPackageGroupPolicy(GetModelPackageGroupPolicyRequest request)
AmazonSageMakerGets 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 AWS Identity and Access Management User Guide..
getModelPackageGroupPolicy in interface AmazonSageMakerpublic GetSagemakerServicecatalogPortfolioStatusResult getSagemakerServicecatalogPortfolioStatus(GetSagemakerServicecatalogPortfolioStatusRequest request)
AmazonSageMakerGets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
getSagemakerServicecatalogPortfolioStatus in interface AmazonSageMakerpublic GetSearchSuggestionsResult getSearchSuggestions(GetSearchSuggestionsRequest request)
AmazonSageMaker
 An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of
 possible matches for the property name to use in Search queries. Provides suggestions for
 HyperParameters, Tags, and Metrics.
 
getSearchSuggestions in interface AmazonSageMakerpublic ListActionsResult listActions(ListActionsRequest request)
AmazonSageMakerLists the actions in your account and their properties.
listActions in interface AmazonSageMakerpublic ListAlgorithmsResult listAlgorithms(ListAlgorithmsRequest request)
AmazonSageMakerLists the machine learning algorithms that have been created.
listAlgorithms in interface AmazonSageMakerpublic ListAppImageConfigsResult listAppImageConfigs(ListAppImageConfigsRequest request)
AmazonSageMakerLists 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.
listAppImageConfigs in interface AmazonSageMakerpublic ListAppsResult listApps(ListAppsRequest request)
AmazonSageMakerLists apps.
listApps in interface AmazonSageMakerpublic ListArtifactsResult listArtifacts(ListArtifactsRequest request)
AmazonSageMakerLists the artifacts in your account and their properties.
listArtifacts in interface AmazonSageMakerpublic ListAssociationsResult listAssociations(ListAssociationsRequest request)
AmazonSageMakerLists the associations in your account and their properties.
listAssociations in interface AmazonSageMakerpublic ListAutoMLJobsResult listAutoMLJobs(ListAutoMLJobsRequest request)
AmazonSageMakerRequest a list of jobs.
listAutoMLJobs in interface AmazonSageMakerpublic ListCandidatesForAutoMLJobResult listCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest request)
AmazonSageMakerList the candidates created for the job.
listCandidatesForAutoMLJob in interface AmazonSageMakerpublic ListCodeRepositoriesResult listCodeRepositories(ListCodeRepositoriesRequest request)
AmazonSageMakerGets a list of the Git repositories in your account.
listCodeRepositories in interface AmazonSageMakerpublic ListCompilationJobsResult listCompilationJobs(ListCompilationJobsRequest request)
AmazonSageMakerLists 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.
listCompilationJobs in interface AmazonSageMakerpublic ListContextsResult listContexts(ListContextsRequest request)
AmazonSageMakerLists the contexts in your account and their properties.
listContexts in interface AmazonSageMakerpublic ListDataQualityJobDefinitionsResult listDataQualityJobDefinitions(ListDataQualityJobDefinitionsRequest request)
AmazonSageMakerLists the data quality job definitions in your account.
listDataQualityJobDefinitions in interface AmazonSageMakerpublic ListDeviceFleetsResult listDeviceFleets(ListDeviceFleetsRequest request)
AmazonSageMakerReturns a list of devices in the fleet.
listDeviceFleets in interface AmazonSageMakerpublic ListDevicesResult listDevices(ListDevicesRequest request)
AmazonSageMakerA list of devices.
listDevices in interface AmazonSageMakerpublic ListDomainsResult listDomains(ListDomainsRequest request)
AmazonSageMakerLists the domains.
listDomains in interface AmazonSageMakerpublic ListEdgePackagingJobsResult listEdgePackagingJobs(ListEdgePackagingJobsRequest request)
AmazonSageMakerReturns a list of edge packaging jobs.
listEdgePackagingJobs in interface AmazonSageMakerpublic ListEndpointConfigsResult listEndpointConfigs(ListEndpointConfigsRequest request)
AmazonSageMakerLists endpoint configurations.
listEndpointConfigs in interface AmazonSageMakerpublic ListEndpointsResult listEndpoints(ListEndpointsRequest request)
AmazonSageMakerLists endpoints.
listEndpoints in interface AmazonSageMakerpublic ListExperimentsResult listExperiments(ListExperimentsRequest request)
AmazonSageMakerLists 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.
listExperiments in interface AmazonSageMakerpublic ListFeatureGroupsResult listFeatureGroups(ListFeatureGroupsRequest request)
AmazonSageMaker
 List FeatureGroups based on given filter and order.
 
listFeatureGroups in interface AmazonSageMakerpublic ListFlowDefinitionsResult listFlowDefinitions(ListFlowDefinitionsRequest request)
AmazonSageMakerReturns information about the flow definitions in your account.
listFlowDefinitions in interface AmazonSageMakerpublic ListHumanTaskUisResult listHumanTaskUis(ListHumanTaskUisRequest request)
AmazonSageMakerReturns information about the human task user interfaces in your account.
listHumanTaskUis in interface AmazonSageMakerpublic ListHyperParameterTuningJobsResult listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest request)
AmazonSageMakerGets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
listHyperParameterTuningJobs in interface AmazonSageMakerpublic ListImageVersionsResult listImageVersions(ListImageVersionsRequest request)
AmazonSageMakerLists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
listImageVersions in interface AmazonSageMakerpublic ListImagesResult listImages(ListImagesRequest request)
AmazonSageMakerLists 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.
listImages in interface AmazonSageMakerpublic ListLabelingJobsResult listLabelingJobs(ListLabelingJobsRequest request)
AmazonSageMakerGets a list of labeling jobs.
listLabelingJobs in interface AmazonSageMakerpublic ListLabelingJobsForWorkteamResult listLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest request)
AmazonSageMakerGets a list of labeling jobs assigned to a specified work team.
listLabelingJobsForWorkteam in interface AmazonSageMakerpublic ListModelBiasJobDefinitionsResult listModelBiasJobDefinitions(ListModelBiasJobDefinitionsRequest request)
AmazonSageMakerLists model bias jobs definitions that satisfy various filters.
listModelBiasJobDefinitions in interface AmazonSageMakerpublic ListModelExplainabilityJobDefinitionsResult listModelExplainabilityJobDefinitions(ListModelExplainabilityJobDefinitionsRequest request)
AmazonSageMakerLists model explainability job definitions that satisfy various filters.
listModelExplainabilityJobDefinitions in interface AmazonSageMakerpublic ListModelPackageGroupsResult listModelPackageGroups(ListModelPackageGroupsRequest request)
AmazonSageMakerGets a list of the model groups in your AWS account.
listModelPackageGroups in interface AmazonSageMakerpublic ListModelPackagesResult listModelPackages(ListModelPackagesRequest request)
AmazonSageMakerLists the model packages that have been created.
listModelPackages in interface AmazonSageMakerpublic ListModelQualityJobDefinitionsResult listModelQualityJobDefinitions(ListModelQualityJobDefinitionsRequest request)
AmazonSageMakerGets a list of model quality monitoring job definitions in your account.
listModelQualityJobDefinitions in interface AmazonSageMakerpublic ListModelsResult listModels(ListModelsRequest request)
AmazonSageMakerLists models created with the CreateModel API.
listModels in interface AmazonSageMakerpublic ListMonitoringExecutionsResult listMonitoringExecutions(ListMonitoringExecutionsRequest request)
AmazonSageMakerReturns list of all monitoring job executions.
listMonitoringExecutions in interface AmazonSageMakerpublic ListMonitoringSchedulesResult listMonitoringSchedules(ListMonitoringSchedulesRequest request)
AmazonSageMakerReturns list of all monitoring schedules.
listMonitoringSchedules in interface AmazonSageMakerpublic ListNotebookInstanceLifecycleConfigsResult listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest request)
AmazonSageMakerLists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
listNotebookInstanceLifecycleConfigs in interface AmazonSageMakerpublic ListNotebookInstancesResult listNotebookInstances(ListNotebookInstancesRequest request)
AmazonSageMakerReturns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
listNotebookInstances in interface AmazonSageMakerpublic ListPipelineExecutionStepsResult listPipelineExecutionSteps(ListPipelineExecutionStepsRequest request)
AmazonSageMaker
 Gets a list of PipeLineExecutionStep objects.
 
listPipelineExecutionSteps in interface AmazonSageMakerpublic ListPipelineExecutionsResult listPipelineExecutions(ListPipelineExecutionsRequest request)
AmazonSageMakerGets a list of the pipeline executions.
listPipelineExecutions in interface AmazonSageMakerpublic ListPipelineParametersForExecutionResult listPipelineParametersForExecution(ListPipelineParametersForExecutionRequest request)
AmazonSageMakerGets a list of parameters for a pipeline execution.
listPipelineParametersForExecution in interface AmazonSageMakerpublic ListPipelinesResult listPipelines(ListPipelinesRequest request)
AmazonSageMakerGets a list of pipelines.
listPipelines in interface AmazonSageMakerpublic ListProcessingJobsResult listProcessingJobs(ListProcessingJobsRequest request)
AmazonSageMakerLists processing jobs that satisfy various filters.
listProcessingJobs in interface AmazonSageMakerpublic ListProjectsResult listProjects(ListProjectsRequest request)
AmazonSageMakerGets a list of the projects in an AWS account.
listProjects in interface AmazonSageMakerpublic ListSubscribedWorkteamsResult listSubscribedWorkteams(ListSubscribedWorkteamsRequest request)
AmazonSageMaker
 Gets a list of the work teams that you are subscribed to in the AWS Marketplace. The list may be empty if no work
 team satisfies the filter specified in the NameContains parameter.
 
listSubscribedWorkteams in interface AmazonSageMakerpublic ListTagsResult listTags(ListTagsRequest request)
AmazonSageMakerReturns the tags for the specified Amazon SageMaker resource.
listTags in interface AmazonSageMakerpublic ListTrainingJobsResult listTrainingJobs(ListTrainingJobsRequest request)
AmazonSageMakerLists 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 ... }
 
 Then, 100 trainings jobs with any status including those other than InProgress are selected first
 (sorted according the creation time, from the latest to the oldest) and those with status InProgress
 are returned.
 
You can quickly test the API using the following AWS CLI code.
 aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress
 
listTrainingJobs in interface AmazonSageMakerpublic ListTrainingJobsForHyperParameterTuningJobResult listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest request)
AmazonSageMakerGets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
listTrainingJobsForHyperParameterTuningJob in interface AmazonSageMakerpublic ListTransformJobsResult listTransformJobs(ListTransformJobsRequest request)
AmazonSageMakerLists transform jobs.
listTransformJobs in interface AmazonSageMakerpublic ListTrialComponentsResult listTrialComponents(ListTrialComponentsRequest request)
AmazonSageMakerLists 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
 
listTrialComponents in interface AmazonSageMakerpublic ListTrialsResult listTrials(ListTrialsRequest request)
AmazonSageMakerLists 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.
listTrials in interface AmazonSageMakerpublic ListUserProfilesResult listUserProfiles(ListUserProfilesRequest request)
AmazonSageMakerLists user profiles.
listUserProfiles in interface AmazonSageMakerpublic ListWorkforcesResult listWorkforces(ListWorkforcesRequest request)
AmazonSageMakerUse this operation to list all private and vendor workforces in an AWS Region. Note that you can only have one private workforce per AWS Region.
listWorkforces in interface AmazonSageMakerpublic ListWorkteamsResult listWorkteams(ListWorkteamsRequest request)
AmazonSageMaker
 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.
 
listWorkteams in interface AmazonSageMakerpublic PutModelPackageGroupPolicyResult putModelPackageGroupPolicy(PutModelPackageGroupPolicyRequest request)
AmazonSageMakerAdds 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 AWS Identity and Access Management User Guide..
putModelPackageGroupPolicy in interface AmazonSageMakerpublic RegisterDevicesResult registerDevices(RegisterDevicesRequest request)
AmazonSageMakerRegister devices.
registerDevices in interface AmazonSageMakerpublic RenderUiTemplateResult renderUiTemplate(RenderUiTemplateRequest request)
AmazonSageMakerRenders the UI template so that you can preview the worker's experience.
renderUiTemplate in interface AmazonSageMakerpublic SearchResult search(SearchRequest request)
AmazonSageMaker
 Finds Amazon 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.
search in interface AmazonSageMakerpublic StartMonitoringScheduleResult startMonitoringSchedule(StartMonitoringScheduleRequest request)
AmazonSageMakerStarts a previously stopped monitoring schedule.
 By default, when you successfully create a new schedule, the status of a monitoring schedule is
 scheduled.
 
startMonitoringSchedule in interface AmazonSageMakerpublic StartNotebookInstanceResult startNotebookInstance(StartNotebookInstanceRequest request)
AmazonSageMaker
 Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
 After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to
 InService. A notebook instance's status must be InService before you can connect to
 your Jupyter notebook.
 
startNotebookInstance in interface AmazonSageMakerpublic StartPipelineExecutionResult startPipelineExecution(StartPipelineExecutionRequest request)
AmazonSageMakerStarts a pipeline execution.
startPipelineExecution in interface AmazonSageMakerpublic StopAutoMLJobResult stopAutoMLJob(StopAutoMLJobRequest request)
AmazonSageMakerA method for forcing the termination of a running job.
stopAutoMLJob in interface AmazonSageMakerpublic StopCompilationJobResult stopCompilationJob(StopCompilationJobRequest request)
AmazonSageMakerStops 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
 CompilationJobSummary$CompilationJobStatus of the job to Stopping. After Amazon SageMaker
 stops the job, it sets the CompilationJobSummary$CompilationJobStatus to Stopped.
 
stopCompilationJob in interface AmazonSageMakerpublic StopEdgePackagingJobResult stopEdgePackagingJob(StopEdgePackagingJobRequest request)
AmazonSageMakerRequest to stop an edge packaging job.
stopEdgePackagingJob in interface AmazonSageMakerpublic StopHyperParameterTuningJobResult stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest request)
AmazonSageMakerStops 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.
 
stopHyperParameterTuningJob in interface AmazonSageMakerpublic StopLabelingJobResult stopLabelingJob(StopLabelingJobRequest request)
AmazonSageMakerStops 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.
stopLabelingJob in interface AmazonSageMakerpublic StopMonitoringScheduleResult stopMonitoringSchedule(StopMonitoringScheduleRequest request)
AmazonSageMakerStops a previously started monitoring schedule.
stopMonitoringSchedule in interface AmazonSageMakerpublic StopNotebookInstanceResult stopNotebookInstance(StopNotebookInstanceRequest request)
AmazonSageMaker
 Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage
 volume from it. Amazon SageMaker preserves the ML storage volume. Amazon 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.
 
stopNotebookInstance in interface AmazonSageMakerpublic StopPipelineExecutionResult stopPipelineExecution(StopPipelineExecutionRequest request)
AmazonSageMakerStops a pipeline execution.
stopPipelineExecution in interface AmazonSageMakerpublic StopProcessingJobResult stopProcessingJob(StopProcessingJobRequest request)
AmazonSageMakerStops a processing job.
stopProcessingJob in interface AmazonSageMakerpublic StopTrainingJobResult stopTrainingJob(StopTrainingJobRequest request)
AmazonSageMaker
 Stops a training job. To stop a job, Amazon 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, Amazon SageMaker changes the status of the job to
 Stopping. After Amazon SageMaker stops the job, it sets the status to Stopped.
 
stopTrainingJob in interface AmazonSageMakerpublic StopTransformJobResult stopTransformJob(StopTransformJobRequest request)
AmazonSageMakerStops a 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 transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
 
stopTransformJob in interface AmazonSageMakerpublic UpdateActionResult updateAction(UpdateActionRequest request)
AmazonSageMakerUpdates an action.
updateAction in interface AmazonSageMakerpublic UpdateAppImageConfigResult updateAppImageConfig(UpdateAppImageConfigRequest request)
AmazonSageMakerUpdates the properties of an AppImageConfig.
updateAppImageConfig in interface AmazonSageMakerpublic UpdateArtifactResult updateArtifact(UpdateArtifactRequest request)
AmazonSageMakerUpdates an artifact.
updateArtifact in interface AmazonSageMakerpublic UpdateCodeRepositoryResult updateCodeRepository(UpdateCodeRepositoryRequest request)
AmazonSageMakerUpdates the specified Git repository with the specified values.
updateCodeRepository in interface AmazonSageMakerpublic UpdateContextResult updateContext(UpdateContextRequest request)
AmazonSageMakerUpdates a context.
updateContext in interface AmazonSageMakerpublic UpdateDeviceFleetResult updateDeviceFleet(UpdateDeviceFleetRequest request)
AmazonSageMakerUpdates a fleet of devices.
updateDeviceFleet in interface AmazonSageMakerpublic UpdateDevicesResult updateDevices(UpdateDevicesRequest request)
AmazonSageMakerUpdates one or more devices in a fleet.
updateDevices in interface AmazonSageMakerpublic UpdateDomainResult updateDomain(UpdateDomainRequest request)
AmazonSageMakerUpdates the default settings for new user profiles in the domain.
updateDomain in interface AmazonSageMakerpublic UpdateEndpointResult updateEndpoint(UpdateEndpointRequest request)
AmazonSageMaker
 Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint,
 and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is
 no availability loss).
 
 When Amazon 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.
 
updateEndpoint in interface AmazonSageMakerpublic UpdateEndpointWeightsAndCapacitiesResult updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest request)
AmazonSageMaker
 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, Amazon 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.
 
updateEndpointWeightsAndCapacities in interface AmazonSageMakerpublic UpdateExperimentResult updateExperiment(UpdateExperimentRequest request)
AmazonSageMakerAdds, updates, or removes the description of an experiment. Updates the display name of an experiment.
updateExperiment in interface AmazonSageMakerpublic UpdateImageResult updateImage(UpdateImageRequest request)
AmazonSageMakerUpdates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
updateImage in interface AmazonSageMakerpublic UpdateModelPackageResult updateModelPackage(UpdateModelPackageRequest request)
AmazonSageMakerUpdates a versioned model.
updateModelPackage in interface AmazonSageMakerpublic UpdateMonitoringScheduleResult updateMonitoringSchedule(UpdateMonitoringScheduleRequest request)
AmazonSageMakerUpdates a previously created schedule.
updateMonitoringSchedule in interface AmazonSageMakerpublic UpdateNotebookInstanceResult updateNotebookInstance(UpdateNotebookInstanceRequest request)
AmazonSageMakerUpdates 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.
updateNotebookInstance in interface AmazonSageMakerpublic UpdateNotebookInstanceLifecycleConfigResult updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest request)
AmazonSageMakerUpdates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
updateNotebookInstanceLifecycleConfig in interface AmazonSageMakerpublic UpdatePipelineResult updatePipeline(UpdatePipelineRequest request)
AmazonSageMakerUpdates a pipeline.
updatePipeline in interface AmazonSageMakerpublic UpdatePipelineExecutionResult updatePipelineExecution(UpdatePipelineExecutionRequest request)
AmazonSageMakerUpdates a pipeline execution.
updatePipelineExecution in interface AmazonSageMakerpublic UpdateTrainingJobResult updateTrainingJob(UpdateTrainingJobRequest request)
AmazonSageMakerUpdate a model training job to request a new Debugger profiling configuration.
updateTrainingJob in interface AmazonSageMakerpublic UpdateTrialResult updateTrial(UpdateTrialRequest request)
AmazonSageMakerUpdates the display name of a trial.
updateTrial in interface AmazonSageMakerpublic UpdateTrialComponentResult updateTrialComponent(UpdateTrialComponentRequest request)
AmazonSageMakerUpdates one or more properties of a trial component.
updateTrialComponent in interface AmazonSageMakerpublic UpdateUserProfileResult updateUserProfile(UpdateUserProfileRequest request)
AmazonSageMakerUpdates a user profile.
updateUserProfile in interface AmazonSageMakerpublic UpdateWorkforceResult updateWorkforce(UpdateWorkforceRequest request)
AmazonSageMakerUse 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.
 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.
 
 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 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 operation.
This operation only applies to private workforces.
updateWorkforce in interface AmazonSageMakerpublic UpdateWorkteamResult updateWorkteam(UpdateWorkteamRequest request)
AmazonSageMakerUpdates an existing work team with new member definitions or description.
updateWorkteam in interface AmazonSageMakerpublic void shutdown()
AmazonSageMakershutdown in interface AmazonSageMakerpublic ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
AmazonSageMakerResponse 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.
getCachedResponseMetadata in interface AmazonSageMakerrequest - The originally executed request.public AmazonSageMakerWaiters waiters()
waiters in interface AmazonSageMaker