@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public interface AmazonSageMakerAsync extends AmazonSageMaker
AsyncHandler can be used to receive
notification when an asynchronous operation completes.
Note: Do not directly implement this interface, new methods are added to it regularly. Extend from
AbstractAmazonSageMakerAsync instead.
Definition of the public APIs exposed by SageMaker
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, waitersFuture<AddTagsResult> addTagsAsync(AddTagsRequest addTagsRequest)
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.
addTagsRequest - Future<AddTagsResult> addTagsAsync(AddTagsRequest addTagsRequest, AsyncHandler<AddTagsRequest,AddTagsResult> asyncHandler)
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.
addTagsRequest - asyncHandler - 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.Future<CreateEndpointResult> createEndpointAsync(CreateEndpointRequest createEndpointRequest)
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.
createEndpointRequest - Future<CreateEndpointResult> createEndpointAsync(CreateEndpointRequest createEndpointRequest, AsyncHandler<CreateEndpointRequest,CreateEndpointResult> asyncHandler)
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.
createEndpointRequest - asyncHandler - 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.Future<CreateEndpointConfigResult> createEndpointConfigAsync(CreateEndpointConfigRequest createEndpointConfigRequest)
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.
createEndpointConfigRequest - Future<CreateEndpointConfigResult> createEndpointConfigAsync(CreateEndpointConfigRequest createEndpointConfigRequest, AsyncHandler<CreateEndpointConfigRequest,CreateEndpointConfigResult> asyncHandler)
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.
createEndpointConfigRequest - asyncHandler - 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.Future<CreateHyperParameterTuningJobResult> createHyperParameterTuningJobAsync(CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest)
Starts a hyperparameter tuning job.
createHyperParameterTuningJobRequest - Future<CreateHyperParameterTuningJobResult> createHyperParameterTuningJobAsync(CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest, AsyncHandler<CreateHyperParameterTuningJobRequest,CreateHyperParameterTuningJobResult> asyncHandler)
Starts a hyperparameter tuning job.
createHyperParameterTuningJobRequest - asyncHandler - 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.Future<CreateModelResult> createModelAsync(CreateModelRequest createModelRequest)
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.
createModelRequest - Future<CreateModelResult> createModelAsync(CreateModelRequest createModelRequest, AsyncHandler<CreateModelRequest,CreateModelResult> asyncHandler)
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.
createModelRequest - asyncHandler - 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.Future<CreateNotebookInstanceResult> createNotebookInstanceAsync(CreateNotebookInstanceRequest createNotebookInstanceRequest)
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:
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.
createNotebookInstanceRequest - Future<CreateNotebookInstanceResult> createNotebookInstanceAsync(CreateNotebookInstanceRequest createNotebookInstanceRequest, AsyncHandler<CreateNotebookInstanceRequest,CreateNotebookInstanceResult> asyncHandler)
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:
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.
createNotebookInstanceRequest - asyncHandler - 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.Future<CreateNotebookInstanceLifecycleConfigResult> createNotebookInstanceLifecycleConfigAsync(CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest)
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.
createNotebookInstanceLifecycleConfigRequest - Future<CreateNotebookInstanceLifecycleConfigResult> createNotebookInstanceLifecycleConfigAsync(CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest, AsyncHandler<CreateNotebookInstanceLifecycleConfigRequest,CreateNotebookInstanceLifecycleConfigResult> asyncHandler)
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.
createNotebookInstanceLifecycleConfigRequest - asyncHandler - 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.Future<CreatePresignedNotebookInstanceUrlResult> createPresignedNotebookInstanceUrlAsync(CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest)
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.
createPresignedNotebookInstanceUrlRequest - Future<CreatePresignedNotebookInstanceUrlResult> createPresignedNotebookInstanceUrlAsync(CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest, AsyncHandler<CreatePresignedNotebookInstanceUrlRequest,CreatePresignedNotebookInstanceUrlResult> asyncHandler)
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.
createPresignedNotebookInstanceUrlRequest - asyncHandler - 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.Future<CreateTrainingJobResult> createTrainingJobAsync(CreateTrainingJobRequest createTrainingJobRequest)
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.
createTrainingJobRequest - Future<CreateTrainingJobResult> createTrainingJobAsync(CreateTrainingJobRequest createTrainingJobRequest, AsyncHandler<CreateTrainingJobRequest,CreateTrainingJobResult> asyncHandler)
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.
createTrainingJobRequest - asyncHandler - 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.Future<CreateTransformJobResult> createTransformJobAsync(CreateTransformJobRequest createTransformJobRequest)
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.
createTransformJobRequest - Future<CreateTransformJobResult> createTransformJobAsync(CreateTransformJobRequest createTransformJobRequest, AsyncHandler<CreateTransformJobRequest,CreateTransformJobResult> asyncHandler)
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.
createTransformJobRequest - asyncHandler - 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.Future<DeleteEndpointResult> deleteEndpointAsync(DeleteEndpointRequest deleteEndpointRequest)
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.
deleteEndpointRequest - Future<DeleteEndpointResult> deleteEndpointAsync(DeleteEndpointRequest deleteEndpointRequest, AsyncHandler<DeleteEndpointRequest,DeleteEndpointResult> asyncHandler)
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.
deleteEndpointRequest - asyncHandler - 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.Future<DeleteEndpointConfigResult> deleteEndpointConfigAsync(DeleteEndpointConfigRequest deleteEndpointConfigRequest)
Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified
configuration. It does not delete endpoints created using the configuration.
deleteEndpointConfigRequest - Future<DeleteEndpointConfigResult> deleteEndpointConfigAsync(DeleteEndpointConfigRequest deleteEndpointConfigRequest, AsyncHandler<DeleteEndpointConfigRequest,DeleteEndpointConfigResult> asyncHandler)
Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified
configuration. It does not delete endpoints created using the configuration.
deleteEndpointConfigRequest - asyncHandler - 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.Future<DeleteModelResult> deleteModelAsync(DeleteModelRequest deleteModelRequest)
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.
deleteModelRequest - Future<DeleteModelResult> deleteModelAsync(DeleteModelRequest deleteModelRequest, AsyncHandler<DeleteModelRequest,DeleteModelResult> asyncHandler)
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.
deleteModelRequest - asyncHandler - 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.Future<DeleteNotebookInstanceResult> deleteNotebookInstanceAsync(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest)
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.
deleteNotebookInstanceRequest - Future<DeleteNotebookInstanceResult> deleteNotebookInstanceAsync(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest, AsyncHandler<DeleteNotebookInstanceRequest,DeleteNotebookInstanceResult> asyncHandler)
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.
deleteNotebookInstanceRequest - asyncHandler - 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.Future<DeleteNotebookInstanceLifecycleConfigResult> deleteNotebookInstanceLifecycleConfigAsync(DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest)
Deletes a notebook instance lifecycle configuration.
deleteNotebookInstanceLifecycleConfigRequest - Future<DeleteNotebookInstanceLifecycleConfigResult> deleteNotebookInstanceLifecycleConfigAsync(DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest, AsyncHandler<DeleteNotebookInstanceLifecycleConfigRequest,DeleteNotebookInstanceLifecycleConfigResult> asyncHandler)
Deletes a notebook instance lifecycle configuration.
deleteNotebookInstanceLifecycleConfigRequest - asyncHandler - 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.Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest deleteTagsRequest)
Deletes the specified tags from an Amazon SageMaker resource.
To list a resource's tags, use the ListTags API.
deleteTagsRequest - Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest deleteTagsRequest, AsyncHandler<DeleteTagsRequest,DeleteTagsResult> asyncHandler)
Deletes the specified tags from an Amazon SageMaker resource.
To list a resource's tags, use the ListTags API.
deleteTagsRequest - asyncHandler - 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.Future<DescribeEndpointResult> describeEndpointAsync(DescribeEndpointRequest describeEndpointRequest)
Returns the description of an endpoint.
describeEndpointRequest - Future<DescribeEndpointResult> describeEndpointAsync(DescribeEndpointRequest describeEndpointRequest, AsyncHandler<DescribeEndpointRequest,DescribeEndpointResult> asyncHandler)
Returns the description of an endpoint.
describeEndpointRequest - asyncHandler - 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.Future<DescribeEndpointConfigResult> describeEndpointConfigAsync(DescribeEndpointConfigRequest describeEndpointConfigRequest)
Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
describeEndpointConfigRequest - Future<DescribeEndpointConfigResult> describeEndpointConfigAsync(DescribeEndpointConfigRequest describeEndpointConfigRequest, AsyncHandler<DescribeEndpointConfigRequest,DescribeEndpointConfigResult> asyncHandler)
Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
describeEndpointConfigRequest - asyncHandler - 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.Future<DescribeHyperParameterTuningJobResult> describeHyperParameterTuningJobAsync(DescribeHyperParameterTuningJobRequest describeHyperParameterTuningJobRequest)
Gets a description of a hyperparameter tuning job.
describeHyperParameterTuningJobRequest - Future<DescribeHyperParameterTuningJobResult> describeHyperParameterTuningJobAsync(DescribeHyperParameterTuningJobRequest describeHyperParameterTuningJobRequest, AsyncHandler<DescribeHyperParameterTuningJobRequest,DescribeHyperParameterTuningJobResult> asyncHandler)
Gets a description of a hyperparameter tuning job.
describeHyperParameterTuningJobRequest - asyncHandler - 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.Future<DescribeModelResult> describeModelAsync(DescribeModelRequest describeModelRequest)
Describes a model that you created using the CreateModel API.
describeModelRequest - Future<DescribeModelResult> describeModelAsync(DescribeModelRequest describeModelRequest, AsyncHandler<DescribeModelRequest,DescribeModelResult> asyncHandler)
Describes a model that you created using the CreateModel API.
describeModelRequest - asyncHandler - 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.Future<DescribeNotebookInstanceResult> describeNotebookInstanceAsync(DescribeNotebookInstanceRequest describeNotebookInstanceRequest)
Returns information about a notebook instance.
describeNotebookInstanceRequest - Future<DescribeNotebookInstanceResult> describeNotebookInstanceAsync(DescribeNotebookInstanceRequest describeNotebookInstanceRequest, AsyncHandler<DescribeNotebookInstanceRequest,DescribeNotebookInstanceResult> asyncHandler)
Returns information about a notebook instance.
describeNotebookInstanceRequest - asyncHandler - 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.Future<DescribeNotebookInstanceLifecycleConfigResult> describeNotebookInstanceLifecycleConfigAsync(DescribeNotebookInstanceLifecycleConfigRequest describeNotebookInstanceLifecycleConfigRequest)
Returns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.
describeNotebookInstanceLifecycleConfigRequest - Future<DescribeNotebookInstanceLifecycleConfigResult> describeNotebookInstanceLifecycleConfigAsync(DescribeNotebookInstanceLifecycleConfigRequest describeNotebookInstanceLifecycleConfigRequest, AsyncHandler<DescribeNotebookInstanceLifecycleConfigRequest,DescribeNotebookInstanceLifecycleConfigResult> asyncHandler)
Returns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.
describeNotebookInstanceLifecycleConfigRequest - asyncHandler - 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.Future<DescribeTrainingJobResult> describeTrainingJobAsync(DescribeTrainingJobRequest describeTrainingJobRequest)
Returns information about a training job.
describeTrainingJobRequest - Future<DescribeTrainingJobResult> describeTrainingJobAsync(DescribeTrainingJobRequest describeTrainingJobRequest, AsyncHandler<DescribeTrainingJobRequest,DescribeTrainingJobResult> asyncHandler)
Returns information about a training job.
describeTrainingJobRequest - asyncHandler - 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.Future<DescribeTransformJobResult> describeTransformJobAsync(DescribeTransformJobRequest describeTransformJobRequest)
Returns information about a transform job.
describeTransformJobRequest - Future<DescribeTransformJobResult> describeTransformJobAsync(DescribeTransformJobRequest describeTransformJobRequest, AsyncHandler<DescribeTransformJobRequest,DescribeTransformJobResult> asyncHandler)
Returns information about a transform job.
describeTransformJobRequest - asyncHandler - 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.Future<ListEndpointConfigsResult> listEndpointConfigsAsync(ListEndpointConfigsRequest listEndpointConfigsRequest)
Lists endpoint configurations.
listEndpointConfigsRequest - Future<ListEndpointConfigsResult> listEndpointConfigsAsync(ListEndpointConfigsRequest listEndpointConfigsRequest, AsyncHandler<ListEndpointConfigsRequest,ListEndpointConfigsResult> asyncHandler)
Lists endpoint configurations.
listEndpointConfigsRequest - asyncHandler - 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.Future<ListEndpointsResult> listEndpointsAsync(ListEndpointsRequest listEndpointsRequest)
Lists endpoints.
listEndpointsRequest - Future<ListEndpointsResult> listEndpointsAsync(ListEndpointsRequest listEndpointsRequest, AsyncHandler<ListEndpointsRequest,ListEndpointsResult> asyncHandler)
Lists endpoints.
listEndpointsRequest - asyncHandler - 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.Future<ListHyperParameterTuningJobsResult> listHyperParameterTuningJobsAsync(ListHyperParameterTuningJobsRequest listHyperParameterTuningJobsRequest)
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
listHyperParameterTuningJobsRequest - Future<ListHyperParameterTuningJobsResult> listHyperParameterTuningJobsAsync(ListHyperParameterTuningJobsRequest listHyperParameterTuningJobsRequest, AsyncHandler<ListHyperParameterTuningJobsRequest,ListHyperParameterTuningJobsResult> asyncHandler)
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
listHyperParameterTuningJobsRequest - asyncHandler - 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.Future<ListModelsResult> listModelsAsync(ListModelsRequest listModelsRequest)
Lists models created with the CreateModel API.
listModelsRequest - Future<ListModelsResult> listModelsAsync(ListModelsRequest listModelsRequest, AsyncHandler<ListModelsRequest,ListModelsResult> asyncHandler)
Lists models created with the CreateModel API.
listModelsRequest - asyncHandler - 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.Future<ListNotebookInstanceLifecycleConfigsResult> listNotebookInstanceLifecycleConfigsAsync(ListNotebookInstanceLifecycleConfigsRequest listNotebookInstanceLifecycleConfigsRequest)
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
listNotebookInstanceLifecycleConfigsRequest - Future<ListNotebookInstanceLifecycleConfigsResult> listNotebookInstanceLifecycleConfigsAsync(ListNotebookInstanceLifecycleConfigsRequest listNotebookInstanceLifecycleConfigsRequest, AsyncHandler<ListNotebookInstanceLifecycleConfigsRequest,ListNotebookInstanceLifecycleConfigsResult> asyncHandler)
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
listNotebookInstanceLifecycleConfigsRequest - asyncHandler - 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.Future<ListNotebookInstancesResult> listNotebookInstancesAsync(ListNotebookInstancesRequest listNotebookInstancesRequest)
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
listNotebookInstancesRequest - Future<ListNotebookInstancesResult> listNotebookInstancesAsync(ListNotebookInstancesRequest listNotebookInstancesRequest, AsyncHandler<ListNotebookInstancesRequest,ListNotebookInstancesResult> asyncHandler)
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
listNotebookInstancesRequest - asyncHandler - 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.Future<ListTagsResult> listTagsAsync(ListTagsRequest listTagsRequest)
Returns the tags for the specified Amazon SageMaker resource.
listTagsRequest - Future<ListTagsResult> listTagsAsync(ListTagsRequest listTagsRequest, AsyncHandler<ListTagsRequest,ListTagsResult> asyncHandler)
Returns the tags for the specified Amazon SageMaker resource.
listTagsRequest - asyncHandler - 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.Future<ListTrainingJobsResult> listTrainingJobsAsync(ListTrainingJobsRequest listTrainingJobsRequest)
Lists training jobs.
listTrainingJobsRequest - Future<ListTrainingJobsResult> listTrainingJobsAsync(ListTrainingJobsRequest listTrainingJobsRequest, AsyncHandler<ListTrainingJobsRequest,ListTrainingJobsResult> asyncHandler)
Lists training jobs.
listTrainingJobsRequest - asyncHandler - 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.Future<ListTrainingJobsForHyperParameterTuningJobResult> listTrainingJobsForHyperParameterTuningJobAsync(ListTrainingJobsForHyperParameterTuningJobRequest listTrainingJobsForHyperParameterTuningJobRequest)
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
listTrainingJobsForHyperParameterTuningJobRequest - Future<ListTrainingJobsForHyperParameterTuningJobResult> listTrainingJobsForHyperParameterTuningJobAsync(ListTrainingJobsForHyperParameterTuningJobRequest listTrainingJobsForHyperParameterTuningJobRequest, AsyncHandler<ListTrainingJobsForHyperParameterTuningJobRequest,ListTrainingJobsForHyperParameterTuningJobResult> asyncHandler)
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
listTrainingJobsForHyperParameterTuningJobRequest - asyncHandler - 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.Future<ListTransformJobsResult> listTransformJobsAsync(ListTransformJobsRequest listTransformJobsRequest)
Lists transform jobs.
listTransformJobsRequest - Future<ListTransformJobsResult> listTransformJobsAsync(ListTransformJobsRequest listTransformJobsRequest, AsyncHandler<ListTransformJobsRequest,ListTransformJobsResult> asyncHandler)
Lists transform jobs.
listTransformJobsRequest - asyncHandler - 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.Future<StartNotebookInstanceResult> startNotebookInstanceAsync(StartNotebookInstanceRequest startNotebookInstanceRequest)
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.
startNotebookInstanceRequest - Future<StartNotebookInstanceResult> startNotebookInstanceAsync(StartNotebookInstanceRequest startNotebookInstanceRequest, AsyncHandler<StartNotebookInstanceRequest,StartNotebookInstanceResult> asyncHandler)
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.
startNotebookInstanceRequest - asyncHandler - 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.Future<StopHyperParameterTuningJobResult> stopHyperParameterTuningJobAsync(StopHyperParameterTuningJobRequest stopHyperParameterTuningJobRequest)
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.
stopHyperParameterTuningJobRequest - Future<StopHyperParameterTuningJobResult> stopHyperParameterTuningJobAsync(StopHyperParameterTuningJobRequest stopHyperParameterTuningJobRequest, AsyncHandler<StopHyperParameterTuningJobRequest,StopHyperParameterTuningJobResult> asyncHandler)
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.
stopHyperParameterTuningJobRequest - asyncHandler - 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.Future<StopNotebookInstanceResult> stopNotebookInstanceAsync(StopNotebookInstanceRequest stopNotebookInstanceRequest)
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.
stopNotebookInstanceRequest - Future<StopNotebookInstanceResult> stopNotebookInstanceAsync(StopNotebookInstanceRequest stopNotebookInstanceRequest, AsyncHandler<StopNotebookInstanceRequest,StopNotebookInstanceResult> asyncHandler)
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.
stopNotebookInstanceRequest - asyncHandler - 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.Future<StopTrainingJobResult> stopTrainingJobAsync(StopTrainingJobRequest stopTrainingJobRequest)
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.
stopTrainingJobRequest - Future<StopTrainingJobResult> stopTrainingJobAsync(StopTrainingJobRequest stopTrainingJobRequest, AsyncHandler<StopTrainingJobRequest,StopTrainingJobResult> asyncHandler)
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.
stopTrainingJobRequest - asyncHandler - 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.Future<StopTransformJobResult> stopTransformJobAsync(StopTransformJobRequest stopTransformJobRequest)
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.
stopTransformJobRequest - Future<StopTransformJobResult> stopTransformJobAsync(StopTransformJobRequest stopTransformJobRequest, AsyncHandler<StopTransformJobRequest,StopTransformJobResult> asyncHandler)
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.
stopTransformJobRequest - asyncHandler - 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.Future<UpdateEndpointResult> updateEndpointAsync(UpdateEndpointRequest updateEndpointRequest)
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.
updateEndpointRequest - Future<UpdateEndpointResult> updateEndpointAsync(UpdateEndpointRequest updateEndpointRequest, AsyncHandler<UpdateEndpointRequest,UpdateEndpointResult> asyncHandler)
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.
updateEndpointRequest - asyncHandler - 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.Future<UpdateEndpointWeightsAndCapacitiesResult> updateEndpointWeightsAndCapacitiesAsync(UpdateEndpointWeightsAndCapacitiesRequest updateEndpointWeightsAndCapacitiesRequest)
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.
updateEndpointWeightsAndCapacitiesRequest - Future<UpdateEndpointWeightsAndCapacitiesResult> updateEndpointWeightsAndCapacitiesAsync(UpdateEndpointWeightsAndCapacitiesRequest updateEndpointWeightsAndCapacitiesRequest, AsyncHandler<UpdateEndpointWeightsAndCapacitiesRequest,UpdateEndpointWeightsAndCapacitiesResult> asyncHandler)
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.
updateEndpointWeightsAndCapacitiesRequest - asyncHandler - 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.Future<UpdateNotebookInstanceResult> updateNotebookInstanceAsync(UpdateNotebookInstanceRequest updateNotebookInstanceRequest)
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.
updateNotebookInstanceRequest - Future<UpdateNotebookInstanceResult> updateNotebookInstanceAsync(UpdateNotebookInstanceRequest updateNotebookInstanceRequest, AsyncHandler<UpdateNotebookInstanceRequest,UpdateNotebookInstanceResult> asyncHandler)
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.
updateNotebookInstanceRequest - asyncHandler - 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.Future<UpdateNotebookInstanceLifecycleConfigResult> updateNotebookInstanceLifecycleConfigAsync(UpdateNotebookInstanceLifecycleConfigRequest updateNotebookInstanceLifecycleConfigRequest)
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
updateNotebookInstanceLifecycleConfigRequest - Future<UpdateNotebookInstanceLifecycleConfigResult> updateNotebookInstanceLifecycleConfigAsync(UpdateNotebookInstanceLifecycleConfigRequest updateNotebookInstanceLifecycleConfigRequest, AsyncHandler<UpdateNotebookInstanceLifecycleConfigRequest,UpdateNotebookInstanceLifecycleConfigResult> asyncHandler)
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
updateNotebookInstanceLifecycleConfigRequest - asyncHandler - 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.