AbstractAmazonSageMaker (AWS SDK for Java

java.lang.Object
- com.amazonaws.services.sagemaker.AbstractAmazonSageMaker

All Implemented Interfaces:

AmazonSageMaker

Direct Known Subclasses:

AbstractAmazonSageMakerAsync
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AbstractAmazonSageMaker
extends Object
implements AmazonSageMaker
```
Abstract implementation of AmazonSageMaker. Convenient method forms pass through to the corresponding overload that takes a request object, which throws an UnsupportedOperationException.

Field Summary
- Fields inherited from interface com.amazonaws.services.sagemaker.AmazonSageMaker
  ENDPOINT_PREFIX

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`AddTagsResult`	`addTags(AddTagsRequest request)` Adds or overwrites one or more tags for the specified Amazon SageMaker resource.
`CreateEndpointResult`	`createEndpoint(CreateEndpointRequest request)` Creates an endpoint using the endpoint configuration specified in the request.
`CreateEndpointConfigResult`	`createEndpointConfig(CreateEndpointConfigRequest request)` Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models.
`CreateHyperParameterTuningJobResult`	`createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request)` Starts a hyperparameter tuning job.
`CreateModelResult`	`createModel(CreateModelRequest request)` Creates a model in Amazon SageMaker.
`CreateNotebookInstanceResult`	`createNotebookInstance(CreateNotebookInstanceRequest request)` Creates an Amazon SageMaker notebook instance.
`CreateNotebookInstanceLifecycleConfigResult`	`createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest request)` Creates a lifecycle configuration that you can associate with a notebook instance.
`CreatePresignedNotebookInstanceUrlResult`	`createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest request)` Returns a URL that you can use to connect to the Jupyter server from a notebook instance.
`CreateTrainingJobResult`	`createTrainingJob(CreateTrainingJobRequest request)` Starts a model training job.
`CreateTransformJobResult`	`createTransformJob(CreateTransformJobRequest request)` Starts a transform job.
`DeleteEndpointResult`	`deleteEndpoint(DeleteEndpointRequest request)` Deletes an endpoint.
`DeleteEndpointConfigResult`	`deleteEndpointConfig(DeleteEndpointConfigRequest request)` Deletes an endpoint configuration.
`DeleteModelResult`	`deleteModel(DeleteModelRequest request)` Deletes a model.
`DeleteNotebookInstanceResult`	`deleteNotebookInstance(DeleteNotebookInstanceRequest request)` Deletes an Amazon SageMaker notebook instance.
`DeleteNotebookInstanceLifecycleConfigResult`	`deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest request)` Deletes a notebook instance lifecycle configuration.
`DeleteTagsResult`	`deleteTags(DeleteTagsRequest request)` Deletes the specified tags from an Amazon SageMaker resource.
`DescribeEndpointResult`	`describeEndpoint(DescribeEndpointRequest request)` Returns the description of an endpoint.
`DescribeEndpointConfigResult`	`describeEndpointConfig(DescribeEndpointConfigRequest request)` Returns the description of an endpoint configuration created using the `CreateEndpointConfig` API.
`DescribeHyperParameterTuningJobResult`	`describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest request)` Gets a description of a hyperparameter tuning job.
`DescribeModelResult`	`describeModel(DescribeModelRequest request)` Describes a model that you created using the `CreateModel` API.
`DescribeNotebookInstanceResult`	`describeNotebookInstance(DescribeNotebookInstanceRequest request)` Returns information about a notebook instance.
`DescribeNotebookInstanceLifecycleConfigResult`	`describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest request)` Returns a description of a notebook instance lifecycle configuration.
`DescribeTrainingJobResult`	`describeTrainingJob(DescribeTrainingJobRequest request)` Returns information about a training job.
`DescribeTransformJobResult`	`describeTransformJob(DescribeTransformJobRequest request)` Returns information about a transform job.
`ResponseMetadata`	`getCachedResponseMetadata(AmazonWebServiceRequest request)` Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected.
`ListEndpointConfigsResult`	`listEndpointConfigs(ListEndpointConfigsRequest request)` Lists endpoint configurations.
`ListEndpointsResult`	`listEndpoints(ListEndpointsRequest request)` Lists endpoints.
`ListHyperParameterTuningJobsResult`	`listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest request)` Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
`ListModelsResult`	`listModels(ListModelsRequest request)` Lists models created with the CreateModel API.
`ListNotebookInstanceLifecycleConfigsResult`	`listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest request)` Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
`ListNotebookInstancesResult`	`listNotebookInstances(ListNotebookInstancesRequest request)` Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
`ListTagsResult`	`listTags(ListTagsRequest request)` Returns the tags for the specified Amazon SageMaker resource.
`ListTrainingJobsResult`	`listTrainingJobs(ListTrainingJobsRequest request)` Lists training jobs.
`ListTrainingJobsForHyperParameterTuningJobResult`	`listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest request)` Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
`ListTransformJobsResult`	`listTransformJobs(ListTransformJobsRequest request)` Lists transform jobs.
`void`	`shutdown()` Shuts down this client object, releasing any resources that might be held open.
`StartNotebookInstanceResult`	`startNotebookInstance(StartNotebookInstanceRequest request)` Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
`StopHyperParameterTuningJobResult`	`stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest request)` Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
`StopNotebookInstanceResult`	`stopNotebookInstance(StopNotebookInstanceRequest request)` Terminates the ML compute instance.
`StopTrainingJobResult`	`stopTrainingJob(StopTrainingJobRequest request)` Stops a training job.
`StopTransformJobResult`	`stopTransformJob(StopTransformJobRequest request)` Stops a transform job.
`UpdateEndpointResult`	`updateEndpoint(UpdateEndpointRequest request)` 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).
`UpdateEndpointWeightsAndCapacitiesResult`	`updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest request)` Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint.
`UpdateNotebookInstanceResult`	`updateNotebookInstance(UpdateNotebookInstanceRequest request)` Updates a notebook instance.
`UpdateNotebookInstanceLifecycleConfigResult`	`updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest request)` Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
`AmazonSageMakerWaiters`	`waiters()`

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - addTags
```
public AddTagsResult addTags(AddTagsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, models, 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 Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
    
    Specified by:
    
    addTags in interface AmazonSageMaker
    
    Returns:
    
    Result of the AddTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createEndpoint
```
public CreateEndpointResult createEndpoint(CreateEndpointRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
    
    Use this API only for hosting models using Amazon SageMaker hosting services.
    
    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 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.
    
    For an example, see Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker.
    
    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 i an AWS Region in the AWS Identity and Access Management User Guide.
    
    Specified by:
    
    createEndpoint in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createEndpointConfig
```
public CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest request)
```
    Description copied from interface: 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 only if you want to use Amazon SageMaker hosting services to deploy models into production.
    
    In the request, you define one or more ProductionVariants, each of which identifies a model. 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.
    
    Specified by:
    
    createEndpointConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createHyperParameterTuningJob
```
public CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Starts a hyperparameter tuning job.
    
    Specified by:
    
    createHyperParameterTuningJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateHyperParameterTuningJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createModel
```
public CreateModelResult createModel(CreateModelRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the docker image containing inference code, artifacts (from prior training), and 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.
    
    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.
    
    Specified by:
    
    createModel in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createNotebookInstance
```
public CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest request)
```
    Description copied from interface: AmazonSageMaker
    Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
    
    In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.
    
    Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.
    
    After receiving the request, Amazon SageMaker does the following:
    1. Creates a network interface in the Amazon SageMaker VPC.
    2. (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.
    3. 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).
    
    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.
    Specified by:
    
    createNotebookInstance in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createNotebookInstanceLifecycleConfig
```
public CreateNotebookInstanceLifecycleConfigResult createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.
    
    Each lifecycle configuration script has a limit of 16384 characters.
    
    The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.
    
    View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].
    
    Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
    
    For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.
    
    Specified by:
    
    createNotebookInstanceLifecycleConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createPresignedNotebookInstanceUrl
```
public CreatePresignedNotebookInstanceUrlResult createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest request)
```
    Description copied from interface: 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.
    
    You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. To restrict access, attach an IAM policy that denies access to this API unless the call comes from an IP address in the specified list to every AWS Identity and Access Management user, group, or role used to access the notebook instance. 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 nbi-ip-filter.
    
    Specified by:
    
    createPresignedNotebookInstanceUrl in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreatePresignedNotebookInstanceUrl operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createTrainingJob
```
public CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
    
    If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a deep learning service other than Amazon SageMaker, provided that you know how to use them for inferences.
    
    In the request body, you provide the following:
    - AlgorithmSpecification - Identifies the training algorithm to use.
    - HyperParameters - Specify these algorithm-specific parameters to influence the quality of the final model. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
    - InputDataConfig - Describes the training dataset and the Amazon S3 location where it is stored.
    - OutputDataConfig - Identifies the Amazon S3 location 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.
    - RoleARN - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training.
    - StoppingCondition - Sets a duration for training. Use this parameter to cap model training costs.
    For more information about Amazon SageMaker, see How It Works.
    Specified by:
    
    createTrainingJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateTrainingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createTransformJob
```
public CreateTransformJobResult createTransformJob(CreateTransformJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
    
    To perform batch transformations, you create a transform job and use the data that you have readily available.
    
    In the request body, you provide the following:
    - TransformJobName - Identifies the transform job. The name must be unique within an AWS Region in an AWS account.
    - ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see CreateModel.
    - TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.
    - TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
    - TransformResources - Identifies the ML compute instances for the transform job.
    For more information about how batch transformation works Amazon SageMaker, see How It Works.
    Specified by:
    
    createTransformJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the CreateTransformJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteEndpoint
```
public DeleteEndpointResult deleteEndpoint(DeleteEndpointRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.
    
    Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
    
    Specified by:
    
    deleteEndpoint in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteEndpointConfig
```
public DeleteEndpointConfigResult deleteEndpointConfig(DeleteEndpointConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.
    
    Specified by:
    
    deleteEndpointConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteModel
```
public DeleteModelResult deleteModel(DeleteModelRequest request)
```
    Description copied from interface: 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.
    
    Specified by:
    
    deleteModel in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteNotebookInstance
```
public DeleteNotebookInstanceResult deleteNotebookInstance(DeleteNotebookInstanceRequest request)
```
    Description copied from interface: 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.
    
    Specified by:
    
    deleteNotebookInstance in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteNotebookInstanceLifecycleConfig
```
public DeleteNotebookInstanceLifecycleConfigResult deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes a notebook instance lifecycle configuration.
    
    Specified by:
    
    deleteNotebookInstanceLifecycleConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteTags
```
public DeleteTagsResult deleteTags(DeleteTagsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Deletes the specified tags from an Amazon SageMaker resource.
    
    To list a resource's tags, use the ListTags API.
    
    Specified by:
    
    deleteTags in interface AmazonSageMaker
    
    Returns:
    
    Result of the DeleteTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeEndpoint
```
public DescribeEndpointResult describeEndpoint(DescribeEndpointRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns the description of an endpoint.
    
    Specified by:
    
    describeEndpoint in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeEndpointConfig
```
public DescribeEndpointConfigResult describeEndpointConfig(DescribeEndpointConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
    
    Specified by:
    
    describeEndpointConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeHyperParameterTuningJob
```
public DescribeHyperParameterTuningJobResult describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets a description of a hyperparameter tuning job.
    
    Specified by:
    
    describeHyperParameterTuningJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeHyperParameterTuningJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeModel
```
public DescribeModelResult describeModel(DescribeModelRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Describes a model that you created using the CreateModel API.
    
    Specified by:
    
    describeModel in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeNotebookInstance
```
public DescribeNotebookInstanceResult describeNotebookInstance(DescribeNotebookInstanceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns information about a notebook instance.
    
    Specified by:
    
    describeNotebookInstance in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeNotebookInstanceLifecycleConfig
```
public DescribeNotebookInstanceLifecycleConfigResult describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns a description of a notebook instance lifecycle configuration.
    
    For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.
    
    Specified by:
    
    describeNotebookInstanceLifecycleConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeTrainingJob
```
public DescribeTrainingJobResult describeTrainingJob(DescribeTrainingJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns information about a training job.
    
    Specified by:
    
    describeTrainingJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeTrainingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeTransformJob
```
public DescribeTransformJobResult describeTransformJob(DescribeTransformJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns information about a transform job.
    
    Specified by:
    
    describeTransformJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the DescribeTransformJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEndpointConfigs
```
public ListEndpointConfigsResult listEndpointConfigs(ListEndpointConfigsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists endpoint configurations.
    
    Specified by:
    
    listEndpointConfigs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListEndpointConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEndpoints
```
public ListEndpointsResult listEndpoints(ListEndpointsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists endpoints.
    
    Specified by:
    
    listEndpoints in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListEndpoints operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listHyperParameterTuningJobs
```
public ListHyperParameterTuningJobsResult listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
    
    Specified by:
    
    listHyperParameterTuningJobs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListHyperParameterTuningJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModels
```
public ListModelsResult listModels(ListModelsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists models created with the CreateModel API.
    
    Specified by:
    
    listModels in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListModels operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listNotebookInstanceLifecycleConfigs
```
public ListNotebookInstanceLifecycleConfigsResult listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
    
    Specified by:
    
    listNotebookInstanceLifecycleConfigs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListNotebookInstanceLifecycleConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listNotebookInstances
```
public ListNotebookInstancesResult listNotebookInstances(ListNotebookInstancesRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
    
    Specified by:
    
    listNotebookInstances in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListNotebookInstances operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTags
```
public ListTagsResult listTags(ListTagsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Returns the tags for the specified Amazon SageMaker resource.
    
    Specified by:
    
    listTags in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrainingJobs
```
public ListTrainingJobsResult listTrainingJobs(ListTrainingJobsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists training jobs.
    
    Specified by:
    
    listTrainingJobs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListTrainingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrainingJobsForHyperParameterTuningJob
```
public ListTrainingJobsForHyperParameterTuningJobResult listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
    
    Specified by:
    
    listTrainingJobsForHyperParameterTuningJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListTrainingJobsForHyperParameterTuningJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTransformJobs
```
public ListTransformJobsResult listTransformJobs(ListTransformJobsRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Lists transform jobs.
    
    Specified by:
    
    listTransformJobs in interface AmazonSageMaker
    
    Returns:
    
    Result of the ListTransformJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - startNotebookInstance
```
public StartNotebookInstanceResult startNotebookInstance(StartNotebookInstanceRequest request)
```
    Description copied from interface: 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.
    
    Specified by:
    
    startNotebookInstance in interface AmazonSageMaker
    
    Returns:
    
    Result of the StartNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopHyperParameterTuningJob
```
public StopHyperParameterTuningJobResult stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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.
    
    Specified by:
    
    stopHyperParameterTuningJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the StopHyperParameterTuningJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopNotebookInstance
```
public StopNotebookInstanceResult stopNotebookInstance(StopNotebookInstanceRequest request)
```
    Description copied from interface: 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.
    
    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.
    
    Specified by:
    
    stopNotebookInstance in interface AmazonSageMaker
    
    Returns:
    
    Result of the StopNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopTrainingJob
```
public StopTrainingJobResult stopTrainingJob(StopTrainingJobRequest request)
```
    Description copied from interface: 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.
    
    Training algorithms provided by Amazon SageMaker save the intermediate results of a model training job. This intermediate data is a valid model artifact. You can use the model artifacts that are saved when Amazon SageMaker stops a training job to create a model.
    
    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.
    
    Specified by:
    
    stopTrainingJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the StopTrainingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopTransformJob
```
public StopTransformJobResult stopTransformJob(StopTransformJobRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Stops 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.
    
    Specified by:
    
    stopTransformJob in interface AmazonSageMaker
    
    Returns:
    
    Result of the StopTransformJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateEndpoint
```
public UpdateEndpointResult updateEndpoint(UpdateEndpointRequest request)
```
    Description copied from interface: 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 cannot update an endpoint with the current EndpointConfig. To update an endpoint, you must create a new EndpointConfig.
    
    Specified by:
    
    updateEndpoint in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateEndpointWeightsAndCapacities
```
public UpdateEndpointWeightsAndCapacitiesResult updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest request)
```
    Description copied from interface: 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.
    
    Specified by:
    
    updateEndpointWeightsAndCapacities in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateEndpointWeightsAndCapacities operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateNotebookInstance
```
public UpdateNotebookInstanceResult updateNotebookInstance(UpdateNotebookInstanceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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. You can also update the VPC security groups.
    
    Specified by:
    
    updateNotebookInstance in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateNotebookInstanceLifecycleConfig
```
public UpdateNotebookInstanceLifecycleConfigResult updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
    
    Specified by:
    
    updateNotebookInstanceLifecycleConfig in interface AmazonSageMaker
    
    Returns:
    
    Result of the UpdateNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - shutdown
```
public void shutdown()
```
    Description copied from interface: AmazonSageMaker
    
    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.
    
    Specified by:
    
    shutdown in interface AmazonSageMaker
  - getCachedResponseMetadata
```
public ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
```
    Description copied from interface: AmazonSageMaker
    
    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.
    
    Specified by:
    
    getCachedResponseMetadata in interface AmazonSageMaker
    
    Parameters:
    
    request - The originally executed request.
    
    Returns:
    
    The response metadata for the specified request, or null if none is available.
  - waiters
```
public AmazonSageMakerWaiters waiters()
```
    Specified by:
    
    waiters in interface AmazonSageMaker

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Class AbstractAmazonSageMaker

Field Summary

Fields inherited from interface com.amazonaws.services.sagemaker.AmazonSageMaker

Method Summary

Methods inherited from class java.lang.Object

Method Detail

addTags

createEndpoint

createEndpointConfig

createHyperParameterTuningJob

createModel

createNotebookInstance

createNotebookInstanceLifecycleConfig

createPresignedNotebookInstanceUrl

createTrainingJob

createTransformJob

deleteEndpoint

deleteEndpointConfig

deleteModel

deleteNotebookInstance

deleteNotebookInstanceLifecycleConfig

deleteTags

describeEndpoint

describeEndpointConfig

describeHyperParameterTuningJob

describeModel

describeNotebookInstance

describeNotebookInstanceLifecycleConfig

describeTrainingJob

describeTransformJob

listEndpointConfigs

listEndpoints

listHyperParameterTuningJobs

listModels

listNotebookInstanceLifecycleConfigs

listNotebookInstances

listTags

listTrainingJobs

listTrainingJobsForHyperParameterTuningJob

listTransformJobs

startNotebookInstance

stopHyperParameterTuningJob

stopNotebookInstance

stopTrainingJob

stopTransformJob

updateEndpoint

updateEndpointWeightsAndCapacities

updateNotebookInstance

updateNotebookInstanceLifecycleConfig

shutdown

getCachedResponseMetadata

waiters