@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class AbstractAmazonSageMakerAsync extends AbstractAmazonSageMaker implements AmazonSageMakerAsync
AmazonSageMakerAsync. Convenient method forms pass through to the corresponding
 overload that takes a request object and an AsyncHandler, which throws an
 UnsupportedOperationException.ENDPOINT_PREFIXaddTags, createEndpoint, createEndpointConfig, createHyperParameterTuningJob, createModel, createNotebookInstance, createNotebookInstanceLifecycleConfig, createPresignedNotebookInstanceUrl, createTrainingJob, createTransformJob, deleteEndpoint, deleteEndpointConfig, deleteModel, deleteNotebookInstance, deleteNotebookInstanceLifecycleConfig, deleteTags, describeEndpoint, describeEndpointConfig, describeHyperParameterTuningJob, describeModel, describeNotebookInstance, describeNotebookInstanceLifecycleConfig, describeTrainingJob, describeTransformJob, getCachedResponseMetadata, listEndpointConfigs, listEndpoints, listHyperParameterTuningJobs, listModels, listNotebookInstanceLifecycleConfigs, listNotebookInstances, listTags, listTrainingJobs, listTrainingJobsForHyperParameterTuningJob, listTransformJobs, shutdown, startNotebookInstance, stopHyperParameterTuningJob, stopNotebookInstance, stopTrainingJob, stopTransformJob, updateEndpoint, updateEndpointWeightsAndCapacities, updateNotebookInstance, updateNotebookInstanceLifecycleConfig, waitersequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitaddTags, createEndpoint, createEndpointConfig, createHyperParameterTuningJob, createModel, createNotebookInstance, createNotebookInstanceLifecycleConfig, createPresignedNotebookInstanceUrl, createTrainingJob, createTransformJob, deleteEndpoint, deleteEndpointConfig, deleteModel, deleteNotebookInstance, deleteNotebookInstanceLifecycleConfig, deleteTags, describeEndpoint, describeEndpointConfig, describeHyperParameterTuningJob, describeModel, describeNotebookInstance, describeNotebookInstanceLifecycleConfig, describeTrainingJob, describeTransformJob, getCachedResponseMetadata, listEndpointConfigs, listEndpoints, listHyperParameterTuningJobs, listModels, listNotebookInstanceLifecycleConfigs, listNotebookInstances, listTags, listTrainingJobs, listTrainingJobsForHyperParameterTuningJob, listTransformJobs, shutdown, startNotebookInstance, stopHyperParameterTuningJob, stopNotebookInstance, stopTrainingJob, stopTransformJob, updateEndpoint, updateEndpointWeightsAndCapacities, updateNotebookInstance, updateNotebookInstanceLifecycleConfig, waiterspublic Future<AddTagsResult> addTagsAsync(AddTagsRequest request)
AmazonSageMakerAsyncAdds 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.
addTagsAsync in interface AmazonSageMakerAsyncpublic Future<AddTagsResult> addTagsAsync(AddTagsRequest request, AsyncHandler<AddTagsRequest,AddTagsResult> asyncHandler)
AmazonSageMakerAsyncAdds 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.
addTagsAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<CreateEndpointResult> createEndpointAsync(CreateEndpointRequest request)
AmazonSageMakerAsyncCreates 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.
createEndpointAsync in interface AmazonSageMakerAsyncpublic Future<CreateEndpointResult> createEndpointAsync(CreateEndpointRequest request, AsyncHandler<CreateEndpointRequest,CreateEndpointResult> asyncHandler)
AmazonSageMakerAsyncCreates 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.
createEndpointAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<CreateEndpointConfigResult> createEndpointConfigAsync(CreateEndpointConfigRequest request)
AmazonSageMakerAsync
 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.
 
createEndpointConfigAsync in interface AmazonSageMakerAsyncpublic Future<CreateEndpointConfigResult> createEndpointConfigAsync(CreateEndpointConfigRequest request, AsyncHandler<CreateEndpointConfigRequest,CreateEndpointConfigResult> asyncHandler)
AmazonSageMakerAsync
 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.
 
createEndpointConfigAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<CreateHyperParameterTuningJobResult> createHyperParameterTuningJobAsync(CreateHyperParameterTuningJobRequest request)
AmazonSageMakerAsyncStarts a hyperparameter tuning job.
createHyperParameterTuningJobAsync in interface AmazonSageMakerAsyncpublic Future<CreateHyperParameterTuningJobResult> createHyperParameterTuningJobAsync(CreateHyperParameterTuningJobRequest request, AsyncHandler<CreateHyperParameterTuningJobRequest,CreateHyperParameterTuningJobResult> asyncHandler)
AmazonSageMakerAsyncStarts a hyperparameter tuning job.
createHyperParameterTuningJobAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<CreateModelResult> createModelAsync(CreateModelRequest request)
AmazonSageMakerAsyncCreates 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.
createModelAsync in interface AmazonSageMakerAsyncpublic Future<CreateModelResult> createModelAsync(CreateModelRequest request, AsyncHandler<CreateModelRequest,CreateModelResult> asyncHandler)
AmazonSageMakerAsyncCreates 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.
createModelAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<CreateNotebookInstanceResult> createNotebookInstanceAsync(CreateNotebookInstanceRequest request)
AmazonSageMakerAsyncCreates 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).
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.
createNotebookInstanceAsync in interface AmazonSageMakerAsyncpublic Future<CreateNotebookInstanceResult> createNotebookInstanceAsync(CreateNotebookInstanceRequest request, AsyncHandler<CreateNotebookInstanceRequest,CreateNotebookInstanceResult> asyncHandler)
AmazonSageMakerAsyncCreates 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).
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.
createNotebookInstanceAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<CreateNotebookInstanceLifecycleConfigResult> createNotebookInstanceLifecycleConfigAsync(CreateNotebookInstanceLifecycleConfigRequest request)
AmazonSageMakerAsyncCreates 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.
createNotebookInstanceLifecycleConfigAsync in interface AmazonSageMakerAsyncpublic Future<CreateNotebookInstanceLifecycleConfigResult> createNotebookInstanceLifecycleConfigAsync(CreateNotebookInstanceLifecycleConfigRequest request, AsyncHandler<CreateNotebookInstanceLifecycleConfigRequest,CreateNotebookInstanceLifecycleConfigResult> asyncHandler)
AmazonSageMakerAsyncCreates 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.
createNotebookInstanceLifecycleConfigAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<CreatePresignedNotebookInstanceUrlResult> createPresignedNotebookInstanceUrlAsync(CreatePresignedNotebookInstanceUrlRequest request)
AmazonSageMakerAsync
 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.
 
createPresignedNotebookInstanceUrlAsync in interface AmazonSageMakerAsyncpublic Future<CreatePresignedNotebookInstanceUrlResult> createPresignedNotebookInstanceUrlAsync(CreatePresignedNotebookInstanceUrlRequest request, AsyncHandler<CreatePresignedNotebookInstanceUrlRequest,CreatePresignedNotebookInstanceUrlResult> asyncHandler)
AmazonSageMakerAsync
 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.
 
createPresignedNotebookInstanceUrlAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<CreateTrainingJobResult> createTrainingJobAsync(CreateTrainingJobRequest request)
AmazonSageMakerAsyncStarts 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.
createTrainingJobAsync in interface AmazonSageMakerAsyncpublic Future<CreateTrainingJobResult> createTrainingJobAsync(CreateTrainingJobRequest request, AsyncHandler<CreateTrainingJobRequest,CreateTrainingJobResult> asyncHandler)
AmazonSageMakerAsyncStarts 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.
createTrainingJobAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<CreateTransformJobResult> createTransformJobAsync(CreateTransformJobRequest request)
AmazonSageMakerAsyncStarts 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.
createTransformJobAsync in interface AmazonSageMakerAsyncpublic Future<CreateTransformJobResult> createTransformJobAsync(CreateTransformJobRequest request, AsyncHandler<CreateTransformJobRequest,CreateTransformJobResult> asyncHandler)
AmazonSageMakerAsyncStarts 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.
createTransformJobAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DeleteEndpointResult> deleteEndpointAsync(DeleteEndpointRequest request)
AmazonSageMakerAsyncDeletes 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.
deleteEndpointAsync in interface AmazonSageMakerAsyncpublic Future<DeleteEndpointResult> deleteEndpointAsync(DeleteEndpointRequest request, AsyncHandler<DeleteEndpointRequest,DeleteEndpointResult> asyncHandler)
AmazonSageMakerAsyncDeletes 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.
deleteEndpointAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DeleteEndpointConfigResult> deleteEndpointConfigAsync(DeleteEndpointConfigRequest request)
AmazonSageMakerAsync
 Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified
 configuration. It does not delete endpoints created using the configuration.
 
deleteEndpointConfigAsync in interface AmazonSageMakerAsyncpublic Future<DeleteEndpointConfigResult> deleteEndpointConfigAsync(DeleteEndpointConfigRequest request, AsyncHandler<DeleteEndpointConfigRequest,DeleteEndpointConfigResult> asyncHandler)
AmazonSageMakerAsync
 Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified
 configuration. It does not delete endpoints created using the configuration.
 
deleteEndpointConfigAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DeleteModelResult> deleteModelAsync(DeleteModelRequest request)
AmazonSageMakerAsync
 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.
 
deleteModelAsync in interface AmazonSageMakerAsyncpublic Future<DeleteModelResult> deleteModelAsync(DeleteModelRequest request, AsyncHandler<DeleteModelRequest,DeleteModelResult> asyncHandler)
AmazonSageMakerAsync
 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.
 
deleteModelAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DeleteNotebookInstanceResult> deleteNotebookInstanceAsync(DeleteNotebookInstanceRequest request)
AmazonSageMakerAsync
 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.
deleteNotebookInstanceAsync in interface AmazonSageMakerAsyncpublic Future<DeleteNotebookInstanceResult> deleteNotebookInstanceAsync(DeleteNotebookInstanceRequest request, AsyncHandler<DeleteNotebookInstanceRequest,DeleteNotebookInstanceResult> asyncHandler)
AmazonSageMakerAsync
 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.
deleteNotebookInstanceAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DeleteNotebookInstanceLifecycleConfigResult> deleteNotebookInstanceLifecycleConfigAsync(DeleteNotebookInstanceLifecycleConfigRequest request)
AmazonSageMakerAsyncDeletes a notebook instance lifecycle configuration.
deleteNotebookInstanceLifecycleConfigAsync in interface AmazonSageMakerAsyncpublic Future<DeleteNotebookInstanceLifecycleConfigResult> deleteNotebookInstanceLifecycleConfigAsync(DeleteNotebookInstanceLifecycleConfigRequest request, AsyncHandler<DeleteNotebookInstanceLifecycleConfigRequest,DeleteNotebookInstanceLifecycleConfigResult> asyncHandler)
AmazonSageMakerAsyncDeletes a notebook instance lifecycle configuration.
deleteNotebookInstanceLifecycleConfigAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest request)
AmazonSageMakerAsyncDeletes the specified tags from an Amazon SageMaker resource.
 To list a resource's tags, use the ListTags API.
 
deleteTagsAsync in interface AmazonSageMakerAsyncpublic Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest request, AsyncHandler<DeleteTagsRequest,DeleteTagsResult> asyncHandler)
AmazonSageMakerAsyncDeletes the specified tags from an Amazon SageMaker resource.
 To list a resource's tags, use the ListTags API.
 
deleteTagsAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DescribeEndpointResult> describeEndpointAsync(DescribeEndpointRequest request)
AmazonSageMakerAsyncReturns the description of an endpoint.
describeEndpointAsync in interface AmazonSageMakerAsyncpublic Future<DescribeEndpointResult> describeEndpointAsync(DescribeEndpointRequest request, AsyncHandler<DescribeEndpointRequest,DescribeEndpointResult> asyncHandler)
AmazonSageMakerAsyncReturns the description of an endpoint.
describeEndpointAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DescribeEndpointConfigResult> describeEndpointConfigAsync(DescribeEndpointConfigRequest request)
AmazonSageMakerAsync
 Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
 
describeEndpointConfigAsync in interface AmazonSageMakerAsyncpublic Future<DescribeEndpointConfigResult> describeEndpointConfigAsync(DescribeEndpointConfigRequest request, AsyncHandler<DescribeEndpointConfigRequest,DescribeEndpointConfigResult> asyncHandler)
AmazonSageMakerAsync
 Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
 
describeEndpointConfigAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DescribeHyperParameterTuningJobResult> describeHyperParameterTuningJobAsync(DescribeHyperParameterTuningJobRequest request)
AmazonSageMakerAsyncGets a description of a hyperparameter tuning job.
describeHyperParameterTuningJobAsync in interface AmazonSageMakerAsyncpublic Future<DescribeHyperParameterTuningJobResult> describeHyperParameterTuningJobAsync(DescribeHyperParameterTuningJobRequest request, AsyncHandler<DescribeHyperParameterTuningJobRequest,DescribeHyperParameterTuningJobResult> asyncHandler)
AmazonSageMakerAsyncGets a description of a hyperparameter tuning job.
describeHyperParameterTuningJobAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DescribeModelResult> describeModelAsync(DescribeModelRequest request)
AmazonSageMakerAsync
 Describes a model that you created using the CreateModel API.
 
describeModelAsync in interface AmazonSageMakerAsyncpublic Future<DescribeModelResult> describeModelAsync(DescribeModelRequest request, AsyncHandler<DescribeModelRequest,DescribeModelResult> asyncHandler)
AmazonSageMakerAsync
 Describes a model that you created using the CreateModel API.
 
describeModelAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DescribeNotebookInstanceResult> describeNotebookInstanceAsync(DescribeNotebookInstanceRequest request)
AmazonSageMakerAsyncReturns information about a notebook instance.
describeNotebookInstanceAsync in interface AmazonSageMakerAsyncpublic Future<DescribeNotebookInstanceResult> describeNotebookInstanceAsync(DescribeNotebookInstanceRequest request, AsyncHandler<DescribeNotebookInstanceRequest,DescribeNotebookInstanceResult> asyncHandler)
AmazonSageMakerAsyncReturns information about a notebook instance.
describeNotebookInstanceAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DescribeNotebookInstanceLifecycleConfigResult> describeNotebookInstanceLifecycleConfigAsync(DescribeNotebookInstanceLifecycleConfigRequest request)
AmazonSageMakerAsyncReturns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.
describeNotebookInstanceLifecycleConfigAsync in interface AmazonSageMakerAsyncpublic Future<DescribeNotebookInstanceLifecycleConfigResult> describeNotebookInstanceLifecycleConfigAsync(DescribeNotebookInstanceLifecycleConfigRequest request, AsyncHandler<DescribeNotebookInstanceLifecycleConfigRequest,DescribeNotebookInstanceLifecycleConfigResult> asyncHandler)
AmazonSageMakerAsyncReturns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.
describeNotebookInstanceLifecycleConfigAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DescribeTrainingJobResult> describeTrainingJobAsync(DescribeTrainingJobRequest request)
AmazonSageMakerAsyncReturns information about a training job.
describeTrainingJobAsync in interface AmazonSageMakerAsyncpublic Future<DescribeTrainingJobResult> describeTrainingJobAsync(DescribeTrainingJobRequest request, AsyncHandler<DescribeTrainingJobRequest,DescribeTrainingJobResult> asyncHandler)
AmazonSageMakerAsyncReturns information about a training job.
describeTrainingJobAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<DescribeTransformJobResult> describeTransformJobAsync(DescribeTransformJobRequest request)
AmazonSageMakerAsyncReturns information about a transform job.
describeTransformJobAsync in interface AmazonSageMakerAsyncpublic Future<DescribeTransformJobResult> describeTransformJobAsync(DescribeTransformJobRequest request, AsyncHandler<DescribeTransformJobRequest,DescribeTransformJobResult> asyncHandler)
AmazonSageMakerAsyncReturns information about a transform job.
describeTransformJobAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<ListEndpointConfigsResult> listEndpointConfigsAsync(ListEndpointConfigsRequest request)
AmazonSageMakerAsyncLists endpoint configurations.
listEndpointConfigsAsync in interface AmazonSageMakerAsyncpublic Future<ListEndpointConfigsResult> listEndpointConfigsAsync(ListEndpointConfigsRequest request, AsyncHandler<ListEndpointConfigsRequest,ListEndpointConfigsResult> asyncHandler)
AmazonSageMakerAsyncLists endpoint configurations.
listEndpointConfigsAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<ListEndpointsResult> listEndpointsAsync(ListEndpointsRequest request)
AmazonSageMakerAsyncLists endpoints.
listEndpointsAsync in interface AmazonSageMakerAsyncpublic Future<ListEndpointsResult> listEndpointsAsync(ListEndpointsRequest request, AsyncHandler<ListEndpointsRequest,ListEndpointsResult> asyncHandler)
AmazonSageMakerAsyncLists endpoints.
listEndpointsAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<ListHyperParameterTuningJobsResult> listHyperParameterTuningJobsAsync(ListHyperParameterTuningJobsRequest request)
AmazonSageMakerAsyncGets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
listHyperParameterTuningJobsAsync in interface AmazonSageMakerAsyncpublic Future<ListHyperParameterTuningJobsResult> listHyperParameterTuningJobsAsync(ListHyperParameterTuningJobsRequest request, AsyncHandler<ListHyperParameterTuningJobsRequest,ListHyperParameterTuningJobsResult> asyncHandler)
AmazonSageMakerAsyncGets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
listHyperParameterTuningJobsAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<ListModelsResult> listModelsAsync(ListModelsRequest request)
AmazonSageMakerAsyncLists models created with the CreateModel API.
listModelsAsync in interface AmazonSageMakerAsyncpublic Future<ListModelsResult> listModelsAsync(ListModelsRequest request, AsyncHandler<ListModelsRequest,ListModelsResult> asyncHandler)
AmazonSageMakerAsyncLists models created with the CreateModel API.
listModelsAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<ListNotebookInstanceLifecycleConfigsResult> listNotebookInstanceLifecycleConfigsAsync(ListNotebookInstanceLifecycleConfigsRequest request)
AmazonSageMakerAsyncLists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
listNotebookInstanceLifecycleConfigsAsync in interface AmazonSageMakerAsyncpublic Future<ListNotebookInstanceLifecycleConfigsResult> listNotebookInstanceLifecycleConfigsAsync(ListNotebookInstanceLifecycleConfigsRequest request, AsyncHandler<ListNotebookInstanceLifecycleConfigsRequest,ListNotebookInstanceLifecycleConfigsResult> asyncHandler)
AmazonSageMakerAsyncLists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
listNotebookInstanceLifecycleConfigsAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<ListNotebookInstancesResult> listNotebookInstancesAsync(ListNotebookInstancesRequest request)
AmazonSageMakerAsyncReturns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
listNotebookInstancesAsync in interface AmazonSageMakerAsyncpublic Future<ListNotebookInstancesResult> listNotebookInstancesAsync(ListNotebookInstancesRequest request, AsyncHandler<ListNotebookInstancesRequest,ListNotebookInstancesResult> asyncHandler)
AmazonSageMakerAsyncReturns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
listNotebookInstancesAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<ListTagsResult> listTagsAsync(ListTagsRequest request)
AmazonSageMakerAsyncReturns the tags for the specified Amazon SageMaker resource.
listTagsAsync in interface AmazonSageMakerAsyncpublic Future<ListTagsResult> listTagsAsync(ListTagsRequest request, AsyncHandler<ListTagsRequest,ListTagsResult> asyncHandler)
AmazonSageMakerAsyncReturns the tags for the specified Amazon SageMaker resource.
listTagsAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<ListTrainingJobsResult> listTrainingJobsAsync(ListTrainingJobsRequest request)
AmazonSageMakerAsyncLists training jobs.
listTrainingJobsAsync in interface AmazonSageMakerAsyncpublic Future<ListTrainingJobsResult> listTrainingJobsAsync(ListTrainingJobsRequest request, AsyncHandler<ListTrainingJobsRequest,ListTrainingJobsResult> asyncHandler)
AmazonSageMakerAsyncLists training jobs.
listTrainingJobsAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<ListTrainingJobsForHyperParameterTuningJobResult> listTrainingJobsForHyperParameterTuningJobAsync(ListTrainingJobsForHyperParameterTuningJobRequest request)
AmazonSageMakerAsyncGets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
listTrainingJobsForHyperParameterTuningJobAsync in interface AmazonSageMakerAsyncpublic Future<ListTrainingJobsForHyperParameterTuningJobResult> listTrainingJobsForHyperParameterTuningJobAsync(ListTrainingJobsForHyperParameterTuningJobRequest request, AsyncHandler<ListTrainingJobsForHyperParameterTuningJobRequest,ListTrainingJobsForHyperParameterTuningJobResult> asyncHandler)
AmazonSageMakerAsyncGets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
listTrainingJobsForHyperParameterTuningJobAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<ListTransformJobsResult> listTransformJobsAsync(ListTransformJobsRequest request)
AmazonSageMakerAsyncLists transform jobs.
listTransformJobsAsync in interface AmazonSageMakerAsyncpublic Future<ListTransformJobsResult> listTransformJobsAsync(ListTransformJobsRequest request, AsyncHandler<ListTransformJobsRequest,ListTransformJobsResult> asyncHandler)
AmazonSageMakerAsyncLists transform jobs.
listTransformJobsAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<StartNotebookInstanceResult> startNotebookInstanceAsync(StartNotebookInstanceRequest request)
AmazonSageMakerAsync
 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.
 
startNotebookInstanceAsync in interface AmazonSageMakerAsyncpublic Future<StartNotebookInstanceResult> startNotebookInstanceAsync(StartNotebookInstanceRequest request, AsyncHandler<StartNotebookInstanceRequest,StartNotebookInstanceResult> asyncHandler)
AmazonSageMakerAsync
 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.
 
startNotebookInstanceAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<StopHyperParameterTuningJobResult> stopHyperParameterTuningJobAsync(StopHyperParameterTuningJobRequest request)
AmazonSageMakerAsyncStops 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.
 
stopHyperParameterTuningJobAsync in interface AmazonSageMakerAsyncpublic Future<StopHyperParameterTuningJobResult> stopHyperParameterTuningJobAsync(StopHyperParameterTuningJobRequest request, AsyncHandler<StopHyperParameterTuningJobRequest,StopHyperParameterTuningJobResult> asyncHandler)
AmazonSageMakerAsyncStops 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.
 
stopHyperParameterTuningJobAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<StopNotebookInstanceResult> stopNotebookInstanceAsync(StopNotebookInstanceRequest request)
AmazonSageMakerAsyncTerminates 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.
 
stopNotebookInstanceAsync in interface AmazonSageMakerAsyncpublic Future<StopNotebookInstanceResult> stopNotebookInstanceAsync(StopNotebookInstanceRequest request, AsyncHandler<StopNotebookInstanceRequest,StopNotebookInstanceResult> asyncHandler)
AmazonSageMakerAsyncTerminates 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.
 
stopNotebookInstanceAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<StopTrainingJobResult> stopTrainingJobAsync(StopTrainingJobRequest request)
AmazonSageMakerAsync
 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.
 
stopTrainingJobAsync in interface AmazonSageMakerAsyncpublic Future<StopTrainingJobResult> stopTrainingJobAsync(StopTrainingJobRequest request, AsyncHandler<StopTrainingJobRequest,StopTrainingJobResult> asyncHandler)
AmazonSageMakerAsync
 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.
 
stopTrainingJobAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<StopTransformJobResult> stopTransformJobAsync(StopTransformJobRequest request)
AmazonSageMakerAsyncStops 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.
 
stopTransformJobAsync in interface AmazonSageMakerAsyncpublic Future<StopTransformJobResult> stopTransformJobAsync(StopTransformJobRequest request, AsyncHandler<StopTransformJobRequest,StopTransformJobResult> asyncHandler)
AmazonSageMakerAsyncStops 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.
 
stopTransformJobAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<UpdateEndpointResult> updateEndpointAsync(UpdateEndpointRequest request)
AmazonSageMakerAsync
 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.
 
updateEndpointAsync in interface AmazonSageMakerAsyncpublic Future<UpdateEndpointResult> updateEndpointAsync(UpdateEndpointRequest request, AsyncHandler<UpdateEndpointRequest,UpdateEndpointResult> asyncHandler)
AmazonSageMakerAsync
 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.
 
updateEndpointAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<UpdateEndpointWeightsAndCapacitiesResult> updateEndpointWeightsAndCapacitiesAsync(UpdateEndpointWeightsAndCapacitiesRequest request)
AmazonSageMakerAsync
 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.
 
updateEndpointWeightsAndCapacitiesAsync in interface AmazonSageMakerAsyncpublic Future<UpdateEndpointWeightsAndCapacitiesResult> updateEndpointWeightsAndCapacitiesAsync(UpdateEndpointWeightsAndCapacitiesRequest request, AsyncHandler<UpdateEndpointWeightsAndCapacitiesRequest,UpdateEndpointWeightsAndCapacitiesResult> asyncHandler)
AmazonSageMakerAsync
 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.
 
updateEndpointWeightsAndCapacitiesAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<UpdateNotebookInstanceResult> updateNotebookInstanceAsync(UpdateNotebookInstanceRequest request)
AmazonSageMakerAsyncUpdates 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.
updateNotebookInstanceAsync in interface AmazonSageMakerAsyncpublic Future<UpdateNotebookInstanceResult> updateNotebookInstanceAsync(UpdateNotebookInstanceRequest request, AsyncHandler<UpdateNotebookInstanceRequest,UpdateNotebookInstanceResult> asyncHandler)
AmazonSageMakerAsyncUpdates 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.
updateNotebookInstanceAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.public Future<UpdateNotebookInstanceLifecycleConfigResult> updateNotebookInstanceLifecycleConfigAsync(UpdateNotebookInstanceLifecycleConfigRequest request)
AmazonSageMakerAsyncUpdates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
updateNotebookInstanceLifecycleConfigAsync in interface AmazonSageMakerAsyncpublic Future<UpdateNotebookInstanceLifecycleConfigResult> updateNotebookInstanceLifecycleConfigAsync(UpdateNotebookInstanceLifecycleConfigRequest request, AsyncHandler<UpdateNotebookInstanceLifecycleConfigRequest,UpdateNotebookInstanceLifecycleConfigResult> asyncHandler)
AmazonSageMakerAsyncUpdates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
updateNotebookInstanceLifecycleConfigAsync in interface AmazonSageMakerAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
        implementation of the callback methods in this interface to receive notification of successful or
        unsuccessful completion of the operation.Copyright © 2013 Amazon Web Services, Inc. All Rights Reserved.