@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.
Provides APIs for creating and managing Amazon SageMaker resources.
Other Resources:
ENDPOINT_PREFIXaddTags, associateTrialComponent, createAlgorithm, createApp, createAutoMLJob, createCodeRepository, createCompilationJob, createDomain, createEndpoint, createEndpointConfig, createExperiment, createFlowDefinition, createHumanTaskUi, createHyperParameterTuningJob, createLabelingJob, createModel, createModelPackage, createMonitoringSchedule, createNotebookInstance, createNotebookInstanceLifecycleConfig, createPresignedDomainUrl, createPresignedNotebookInstanceUrl, createProcessingJob, createTrainingJob, createTransformJob, createTrial, createTrialComponent, createUserProfile, createWorkteam, deleteAlgorithm, deleteApp, deleteCodeRepository, deleteDomain, deleteEndpoint, deleteEndpointConfig, deleteExperiment, deleteFlowDefinition, deleteHumanTaskUi, deleteModel, deleteModelPackage, deleteMonitoringSchedule, deleteNotebookInstance, deleteNotebookInstanceLifecycleConfig, deleteTags, deleteTrial, deleteTrialComponent, deleteUserProfile, deleteWorkteam, describeAlgorithm, describeApp, describeAutoMLJob, describeCodeRepository, describeCompilationJob, describeDomain, describeEndpoint, describeEndpointConfig, describeExperiment, describeFlowDefinition, describeHumanTaskUi, describeHyperParameterTuningJob, describeLabelingJob, describeModel, describeModelPackage, describeMonitoringSchedule, describeNotebookInstance, describeNotebookInstanceLifecycleConfig, describeProcessingJob, describeSubscribedWorkteam, describeTrainingJob, describeTransformJob, describeTrial, describeTrialComponent, describeUserProfile, describeWorkforce, describeWorkteam, disassociateTrialComponent, getCachedResponseMetadata, getSearchSuggestions, listAlgorithms, listApps, listAutoMLJobs, listCandidatesForAutoMLJob, listCodeRepositories, listCompilationJobs, listDomains, listEndpointConfigs, listEndpoints, listExperiments, listFlowDefinitions, listHumanTaskUis, listHyperParameterTuningJobs, listLabelingJobs, listLabelingJobsForWorkteam, listModelPackages, listModels, listMonitoringExecutions, listMonitoringSchedules, listNotebookInstanceLifecycleConfigs, listNotebookInstances, listProcessingJobs, listSubscribedWorkteams, listTags, listTrainingJobs, listTrainingJobsForHyperParameterTuningJob, listTransformJobs, listTrialComponents, listTrials, listUserProfiles, listWorkteams, renderUiTemplate, search, shutdown, startMonitoringSchedule, startNotebookInstance, stopAutoMLJob, stopCompilationJob, stopHyperParameterTuningJob, stopLabelingJob, stopMonitoringSchedule, stopNotebookInstance, stopProcessingJob, stopTrainingJob, stopTransformJob, updateCodeRepository, updateDomain, updateEndpoint, updateEndpointWeightsAndCapacities, updateExperiment, updateMonitoringSchedule, updateNotebookInstance, updateNotebookInstanceLifecycleConfig, updateTrial, updateTrialComponent, updateUserProfile, updateWorkforce, updateWorkteam, 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, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see AWS Tagging Strategies.
Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the
hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter
tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter
tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you
first create the tuning job by specifying them in the Tags parameter of
CreateHyperParameterTuningJob
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, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see AWS Tagging Strategies.
Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the
hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter
tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter
tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you
first create the tuning job by specifying them in the Tags parameter of
CreateHyperParameterTuningJob
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<AssociateTrialComponentResult> associateTrialComponentAsync(AssociateTrialComponentRequest associateTrialComponentRequest)
Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
associateTrialComponentRequest - Future<AssociateTrialComponentResult> associateTrialComponentAsync(AssociateTrialComponentRequest associateTrialComponentRequest, AsyncHandler<AssociateTrialComponentRequest,AssociateTrialComponentResult> asyncHandler)
Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
associateTrialComponentRequest - 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<CreateAlgorithmResult> createAlgorithmAsync(CreateAlgorithmRequest createAlgorithmRequest)
Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
createAlgorithmRequest - Future<CreateAlgorithmResult> createAlgorithmAsync(CreateAlgorithmRequest createAlgorithmRequest, AsyncHandler<CreateAlgorithmRequest,CreateAlgorithmResult> asyncHandler)
Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
createAlgorithmRequest - 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<CreateAppResult> createAppAsync(CreateAppRequest createAppRequest)
Creates a running App for the specified UserProfile. Supported Apps are JupyterServer and KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
createAppRequest - Future<CreateAppResult> createAppAsync(CreateAppRequest createAppRequest, AsyncHandler<CreateAppRequest,CreateAppResult> asyncHandler)
Creates a running App for the specified UserProfile. Supported Apps are JupyterServer and KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
createAppRequest - 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<CreateAutoMLJobResult> createAutoMLJobAsync(CreateAutoMLJobRequest createAutoMLJobRequest)
Creates an AutoPilot job.
After you run an AutoPilot job, you can find the best performing model by calling , and then deploy that model by following the steps described in Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services.
For information about how to use AutoPilot, see Use AutoPilot to Automate Model Development.
createAutoMLJobRequest - Future<CreateAutoMLJobResult> createAutoMLJobAsync(CreateAutoMLJobRequest createAutoMLJobRequest, AsyncHandler<CreateAutoMLJobRequest,CreateAutoMLJobResult> asyncHandler)
Creates an AutoPilot job.
After you run an AutoPilot job, you can find the best performing model by calling , and then deploy that model by following the steps described in Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services.
For information about how to use AutoPilot, see Use AutoPilot to Automate Model Development.
createAutoMLJobRequest - 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<CreateCodeRepositoryResult> createCodeRepositoryAsync(CreateCodeRepositoryRequest createCodeRepositoryRequest)
Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in AWS CodeCommit or in any other Git repository.
createCodeRepositoryRequest - Future<CreateCodeRepositoryResult> createCodeRepositoryAsync(CreateCodeRepositoryRequest createCodeRepositoryRequest, AsyncHandler<CreateCodeRepositoryRequest,CreateCodeRepositoryResult> asyncHandler)
Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in AWS CodeCommit or in any other Git repository.
createCodeRepositoryRequest - 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<CreateCompilationJobResult> createCompilationJobAsync(CreateCompilationJobRequest createCompilationJobRequest)
Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with AWS IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job
You can also provide a Tag to track the model compilation job's resource use and costs. The response
body contains the CompilationJobArn for the compiled job.
To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
createCompilationJobRequest - Future<CreateCompilationJobResult> createCompilationJobAsync(CreateCompilationJobRequest createCompilationJobRequest, AsyncHandler<CreateCompilationJobRequest,CreateCompilationJobResult> asyncHandler)
Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with AWS IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job
You can also provide a Tag to track the model compilation job's resource use and costs. The response
body contains the CompilationJobArn for the compiled job.
To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
createCompilationJobRequest - 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<CreateDomainResult> createDomainAsync(CreateDomainRequest createDomainRequest)
Creates a Domain used by SageMaker Studio. A domain consists of an associated directory, a list of
authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC)
configurations. An AWS account is limited to one domain per region. Users within a domain can share notebook
files and other artifacts with each other.
When a domain is created, an Amazon Elastic File System (EFS) volume is also created for use by all of the users within the domain. Each user receives a private home directory within the EFS for notebooks, Git repositories, and data files.
All traffic between the domain and the EFS volume is communicated through the specified subnet IDs. All other traffic goes over the Internet through an Amazon SageMaker system VPC. The EFS traffic uses the NFS/TCP protocol over port 2049.
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a SageMaker Studio app successfully.
createDomainRequest - Future<CreateDomainResult> createDomainAsync(CreateDomainRequest createDomainRequest, AsyncHandler<CreateDomainRequest,CreateDomainResult> asyncHandler)
Creates a Domain used by SageMaker Studio. A domain consists of an associated directory, a list of
authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC)
configurations. An AWS account is limited to one domain per region. Users within a domain can share notebook
files and other artifacts with each other.
When a domain is created, an Amazon Elastic File System (EFS) volume is also created for use by all of the users within the domain. Each user receives a private home directory within the EFS for notebooks, Git repositories, and data files.
All traffic between the domain and the EFS volume is communicated through the specified subnet IDs. All other traffic goes over the Internet through an Amazon SageMaker system VPC. The EFS traffic uses the NFS/TCP protocol over port 2049.
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a SageMaker Studio app successfully.
createDomainRequest - 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 to deploy models using Amazon SageMaker hosting services.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
You must not delete an EndpointConfig that is in use by an endpoint that is live or while the
UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To
update an endpoint, you must create a new EndpointConfig.
The endpoint name must be unique within an AWS Region in your AWS account.
When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
exists. When you read data from a DynamoDB table supporting
Eventually Consistent Reads , the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
this causes a validation error. If you repeat your read request after a short time, the response should return
the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
DynamoDB eventually consistent read.
When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it
creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming
requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
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 to deploy models using Amazon SageMaker hosting services.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
You must not delete an EndpointConfig that is in use by an endpoint that is live or while the
UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To
update an endpoint, you must create a new EndpointConfig.
The endpoint name must be unique within an AWS Region in your AWS account.
When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
exists. When you read data from a DynamoDB table supporting
Eventually Consistent Reads , the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
this causes a validation error. If you repeat your read request after a short time, the response should return
the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
DynamoDB eventually consistent read.
When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it
creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming
requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
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 if you want to use Amazon SageMaker hosting services to deploy models into production.
In the request, you define a ProductionVariant, for each model that you want to deploy. Each
ProductionVariant parameter also describes the resources that you want Amazon SageMaker to
provision. This includes the number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you
want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign
traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model
A, and one-third to model B.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
exists. When you read data from a DynamoDB table supporting
Eventually Consistent Reads , the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
this causes a validation error. If you repeat your read request after a short time, the response should return
the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
DynamoDB eventually consistent read.
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 if you want to use Amazon SageMaker hosting services to deploy models into production.
In the request, you define a ProductionVariant, for each model that you want to deploy. Each
ProductionVariant parameter also describes the resources that you want Amazon SageMaker to
provision. This includes the number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you
want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign
traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model
A, and one-third to model B.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
exists. When you read data from a DynamoDB table supporting
Eventually Consistent Reads , the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
this causes a validation error. If you repeat your read request after a short time, the response should return
the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
DynamoDB eventually consistent read.
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<CreateExperimentResult> createExperimentAsync(CreateExperimentRequest createExperimentRequest)
Creates an Amazon SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.
The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
To add a description to an experiment, specify the optional Description parameter. To add a
description later, or to change the description, call the UpdateExperiment API.
To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
createExperimentRequest - Future<CreateExperimentResult> createExperimentAsync(CreateExperimentRequest createExperimentRequest, AsyncHandler<CreateExperimentRequest,CreateExperimentResult> asyncHandler)
Creates an Amazon SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.
The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
To add a description to an experiment, specify the optional Description parameter. To add a
description later, or to change the description, call the UpdateExperiment API.
To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
createExperimentRequest - 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<CreateFlowDefinitionResult> createFlowDefinitionAsync(CreateFlowDefinitionRequest createFlowDefinitionRequest)
Creates a flow definition.
createFlowDefinitionRequest - Future<CreateFlowDefinitionResult> createFlowDefinitionAsync(CreateFlowDefinitionRequest createFlowDefinitionRequest, AsyncHandler<CreateFlowDefinitionRequest,CreateFlowDefinitionResult> asyncHandler)
Creates a flow definition.
createFlowDefinitionRequest - 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<CreateHumanTaskUiResult> createHumanTaskUiAsync(CreateHumanTaskUiRequest createHumanTaskUiRequest)
Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
createHumanTaskUiRequest - Future<CreateHumanTaskUiResult> createHumanTaskUiAsync(CreateHumanTaskUiRequest createHumanTaskUiRequest, AsyncHandler<CreateHumanTaskUiRequest,CreateHumanTaskUiResult> asyncHandler)
Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
createHumanTaskUiRequest - 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. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
createHyperParameterTuningJobRequest - Future<CreateHyperParameterTuningJobResult> createHyperParameterTuningJobAsync(CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest, AsyncHandler<CreateHyperParameterTuningJobRequest,CreateHyperParameterTuningJobResult> asyncHandler)
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
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<CreateLabelingJobResult> createLabelingJobAsync(CreateLabelingJobRequest createLabelingJobRequest)
Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
You can select your workforce from one of three providers:
A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas.
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
createLabelingJobRequest - Future<CreateLabelingJobResult> createLabelingJobAsync(CreateLabelingJobRequest createLabelingJobRequest, AsyncHandler<CreateLabelingJobRequest,CreateLabelingJobResult> asyncHandler)
Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
You can select your workforce from one of three providers:
A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas.
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
createLabelingJobRequest - 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 that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then
create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers
that you defined for the model in the hosting environment.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon
SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the CreateModel request, you must define a container with the PrimaryContainer
parameter.
In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.
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 that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then
create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers
that you defined for the model in the hosting environment.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon
SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the CreateModel request, you must define a container with the PrimaryContainer
parameter.
In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.
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<CreateModelPackageResult> createModelPackageAsync(CreateModelPackageRequest createModelPackageRequest)
Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
To create a model package by specifying a Docker container that contains your inference code and the Amazon S3
location of your model artifacts, provide values for InferenceSpecification. To create a model from
an algorithm resource that you created or subscribed to in AWS Marketplace, provide a value for
SourceAlgorithmSpecification.
createModelPackageRequest - Future<CreateModelPackageResult> createModelPackageAsync(CreateModelPackageRequest createModelPackageRequest, AsyncHandler<CreateModelPackageRequest,CreateModelPackageResult> asyncHandler)
Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
To create a model package by specifying a Docker container that contains your inference code and the Amazon S3
location of your model artifacts, provide values for InferenceSpecification. To create a model from
an algorithm resource that you created or subscribed to in AWS Marketplace, provide a value for
SourceAlgorithmSpecification.
createModelPackageRequest - 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<CreateMonitoringScheduleResult> createMonitoringScheduleAsync(CreateMonitoringScheduleRequest createMonitoringScheduleRequest)
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
createMonitoringScheduleRequest - Future<CreateMonitoringScheduleResult> createMonitoringScheduleAsync(CreateMonitoringScheduleRequest createMonitoringScheduleRequest, AsyncHandler<CreateMonitoringScheduleRequest,CreateMonitoringScheduleResult> asyncHandler)
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
createMonitoringScheduleRequest - 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). You can't change the name of a notebook instance after you create it.
After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
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). You can't change the name of a notebook instance after you create it.
After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
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 Step 2.1: (Optional) Customize a Notebook Instance.
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 Step 2.1: (Optional) Customize a Notebook Instance.
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<CreatePresignedDomainUrlResult> createPresignedDomainUrlAsync(CreatePresignedDomainUrlRequest createPresignedDomainUrlRequest)
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
createPresignedDomainUrlRequest - Future<CreatePresignedDomainUrlResult> createPresignedDomainUrlAsync(CreatePresignedDomainUrlRequest createPresignedDomainUrlRequest, AsyncHandler<CreatePresignedDomainUrlRequest,CreatePresignedDomainUrlResult> asyncHandler)
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
createPresignedDomainUrlRequest - 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.
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify.
Use the NotIpAddress condition operator and the aws:SourceIP condition context key to
specify the list of IP addresses that you want to have access to the notebook instance. For more information, see
Limit Access to a Notebook Instance by IP Address.
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
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.
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify.
Use the NotIpAddress condition operator and the aws:SourceIP condition context key to
specify the list of IP addresses that you want to have access to the notebook instance. For more information, see
Limit Access to a Notebook Instance by IP Address.
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
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<CreateProcessingJobResult> createProcessingJobAsync(CreateProcessingJobRequest createProcessingJobRequest)
Creates a processing job.
createProcessingJobRequest - Future<CreateProcessingJobResult> createProcessingJobAsync(CreateProcessingJobRequest createProcessingJobRequest, AsyncHandler<CreateProcessingJobRequest,CreateProcessingJobResult> asyncHandler)
Creates a processing job.
createProcessingJobRequest - 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 machine 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 enable the estimation of model
parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of
hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
InputDataConfig - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is
stored.
OutputDataConfig - Identifies the Amazon S3 bucket where you want Amazon SageMaker to save the
results of model training.
ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy
for model training. In distributed training, you specify more than one instance.
EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by
using Amazon EC2 Spot instances. For more information, see Managed Spot
Training.
RoleARN - The Amazon Resource 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 - To help cap training costs, use MaxRuntimeInSeconds to set a time
limit for training. Use MaxWaitTimeInSeconds to specify how long you are willing to wait for a
managed spot training job to complete.
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 machine 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 enable the estimation of model
parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of
hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
InputDataConfig - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is
stored.
OutputDataConfig - Identifies the Amazon S3 bucket where you want Amazon SageMaker to save the
results of model training.
ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy
for model training. In distributed training, you specify more than one instance.
EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by
using Amazon EC2 Spot instances. For more information, see Managed Spot
Training.
RoleARN - The Amazon Resource 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 - To help cap training costs, use MaxRuntimeInSeconds to set a time
limit for training. Use MaxWaitTimeInSeconds to specify how long you are willing to wait for a
managed spot training job to complete.
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, see Batch Transform.
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, see Batch Transform.
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<CreateTrialResult> createTrialAsync(CreateTrialRequest createTrialRequest)
Creates an Amazon SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single Amazon SageMaker experiment.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
createTrialRequest - Future<CreateTrialResult> createTrialAsync(CreateTrialRequest createTrialRequest, AsyncHandler<CreateTrialRequest,CreateTrialResult> asyncHandler)
Creates an Amazon SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single Amazon SageMaker experiment.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
createTrialRequest - 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<CreateTrialComponentResult> createTrialComponentAsync(CreateTrialComponentRequest createTrialComponentRequest)
Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
CreateTrialComponent can only be invoked from within an Amazon SageMaker managed environment. This
includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call
to CreateTrialComponent from outside one of these environments results in an error.
createTrialComponentRequest - Future<CreateTrialComponentResult> createTrialComponentAsync(CreateTrialComponentRequest createTrialComponentRequest, AsyncHandler<CreateTrialComponentRequest,CreateTrialComponentResult> asyncHandler)
Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
CreateTrialComponent can only be invoked from within an Amazon SageMaker managed environment. This
includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call
to CreateTrialComponent from outside one of these environments results in an error.
createTrialComponentRequest - 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<CreateUserProfileResult> createUserProfileAsync(CreateUserProfileRequest createUserProfileRequest)
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
createUserProfileRequest - Future<CreateUserProfileResult> createUserProfileAsync(CreateUserProfileRequest createUserProfileRequest, AsyncHandler<CreateUserProfileRequest,CreateUserProfileResult> asyncHandler)
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
createUserProfileRequest - 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<CreateWorkteamResult> createWorkteamAsync(CreateWorkteamRequest createWorkteamRequest)
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
You cannot create more than 25 work teams in an account and region.
createWorkteamRequest - Future<CreateWorkteamResult> createWorkteamAsync(CreateWorkteamRequest createWorkteamRequest, AsyncHandler<CreateWorkteamRequest,CreateWorkteamResult> asyncHandler)
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
You cannot create more than 25 work teams in an account and region.
createWorkteamRequest - 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<DeleteAlgorithmResult> deleteAlgorithmAsync(DeleteAlgorithmRequest deleteAlgorithmRequest)
Removes the specified algorithm from your account.
deleteAlgorithmRequest - Future<DeleteAlgorithmResult> deleteAlgorithmAsync(DeleteAlgorithmRequest deleteAlgorithmRequest, AsyncHandler<DeleteAlgorithmRequest,DeleteAlgorithmResult> asyncHandler)
Removes the specified algorithm from your account.
deleteAlgorithmRequest - 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<DeleteAppResult> deleteAppAsync(DeleteAppRequest deleteAppRequest)
Used to stop and delete an app.
deleteAppRequest - Future<DeleteAppResult> deleteAppAsync(DeleteAppRequest deleteAppRequest, AsyncHandler<DeleteAppRequest,DeleteAppResult> asyncHandler)
Used to stop and delete an app.
deleteAppRequest - 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<DeleteCodeRepositoryResult> deleteCodeRepositoryAsync(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest)
Deletes the specified Git repository from your account.
deleteCodeRepositoryRequest - Future<DeleteCodeRepositoryResult> deleteCodeRepositoryAsync(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest, AsyncHandler<DeleteCodeRepositoryRequest,DeleteCodeRepositoryResult> asyncHandler)
Deletes the specified Git repository from your account.
deleteCodeRepositoryRequest - 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<DeleteDomainResult> deleteDomainAsync(DeleteDomainRequest deleteDomainRequest)
Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
deleteDomainRequest - Future<DeleteDomainResult> deleteDomainAsync(DeleteDomainRequest deleteDomainRequest, AsyncHandler<DeleteDomainRequest,DeleteDomainResult> asyncHandler)
Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
deleteDomainRequest - 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.
You must not delete an EndpointConfig in use by an endpoint that is live or while the
UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you
delete the EndpointConfig of an endpoint that is active or being created or updated you may lose
visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring
charges.
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.
You must not delete an EndpointConfig in use by an endpoint that is live or while the
UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you
delete the EndpointConfig of an endpoint that is active or being created or updated you may lose
visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring
charges.
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<DeleteExperimentResult> deleteExperimentAsync(DeleteExperimentRequest deleteExperimentRequest)
Deletes an Amazon SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
deleteExperimentRequest - Future<DeleteExperimentResult> deleteExperimentAsync(DeleteExperimentRequest deleteExperimentRequest, AsyncHandler<DeleteExperimentRequest,DeleteExperimentResult> asyncHandler)
Deletes an Amazon SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
deleteExperimentRequest - 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<DeleteFlowDefinitionResult> deleteFlowDefinitionAsync(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest)
Deletes the specified flow definition.
deleteFlowDefinitionRequest - Future<DeleteFlowDefinitionResult> deleteFlowDefinitionAsync(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest, AsyncHandler<DeleteFlowDefinitionRequest,DeleteFlowDefinitionResult> asyncHandler)
Deletes the specified flow definition.
deleteFlowDefinitionRequest - 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<DeleteHumanTaskUiResult> deleteHumanTaskUiAsync(DeleteHumanTaskUiRequest deleteHumanTaskUiRequest)
Use this operation to delete a worker task template (HumanTaskUi).
To see a list of human task user interfaces (work task templates) in your account, use . When you delete a worker
task template, it no longer appears when you call ListHumanTaskUis.
deleteHumanTaskUiRequest - Future<DeleteHumanTaskUiResult> deleteHumanTaskUiAsync(DeleteHumanTaskUiRequest deleteHumanTaskUiRequest, AsyncHandler<DeleteHumanTaskUiRequest,DeleteHumanTaskUiResult> asyncHandler)
Use this operation to delete a worker task template (HumanTaskUi).
To see a list of human task user interfaces (work task templates) in your account, use . When you delete a worker
task template, it no longer appears when you call ListHumanTaskUis.
deleteHumanTaskUiRequest - 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<DeleteModelPackageResult> deleteModelPackageAsync(DeleteModelPackageRequest deleteModelPackageRequest)
Deletes a model package.
A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
deleteModelPackageRequest - Future<DeleteModelPackageResult> deleteModelPackageAsync(DeleteModelPackageRequest deleteModelPackageRequest, AsyncHandler<DeleteModelPackageRequest,DeleteModelPackageResult> asyncHandler)
Deletes a model package.
A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
deleteModelPackageRequest - 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<DeleteMonitoringScheduleResult> deleteMonitoringScheduleAsync(DeleteMonitoringScheduleRequest deleteMonitoringScheduleRequest)
Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
deleteMonitoringScheduleRequest - Future<DeleteMonitoringScheduleResult> deleteMonitoringScheduleAsync(DeleteMonitoringScheduleRequest deleteMonitoringScheduleRequest, AsyncHandler<DeleteMonitoringScheduleRequest,DeleteMonitoringScheduleResult> asyncHandler)
Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
deleteMonitoringScheduleRequest - 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.
When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.
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.
When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.
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<DeleteTrialResult> deleteTrialAsync(DeleteTrialRequest deleteTrialRequest)
Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
deleteTrialRequest - Future<DeleteTrialResult> deleteTrialAsync(DeleteTrialRequest deleteTrialRequest, AsyncHandler<DeleteTrialRequest,DeleteTrialResult> asyncHandler)
Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
deleteTrialRequest - 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<DeleteTrialComponentResult> deleteTrialComponentAsync(DeleteTrialComponentRequest deleteTrialComponentRequest)
Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
deleteTrialComponentRequest - Future<DeleteTrialComponentResult> deleteTrialComponentAsync(DeleteTrialComponentRequest deleteTrialComponentRequest, AsyncHandler<DeleteTrialComponentRequest,DeleteTrialComponentResult> asyncHandler)
Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
deleteTrialComponentRequest - 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<DeleteUserProfileResult> deleteUserProfileAsync(DeleteUserProfileRequest deleteUserProfileRequest)
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
deleteUserProfileRequest - Future<DeleteUserProfileResult> deleteUserProfileAsync(DeleteUserProfileRequest deleteUserProfileRequest, AsyncHandler<DeleteUserProfileRequest,DeleteUserProfileResult> asyncHandler)
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
deleteUserProfileRequest - 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<DeleteWorkteamResult> deleteWorkteamAsync(DeleteWorkteamRequest deleteWorkteamRequest)
Deletes an existing work team. This operation can't be undone.
deleteWorkteamRequest - Future<DeleteWorkteamResult> deleteWorkteamAsync(DeleteWorkteamRequest deleteWorkteamRequest, AsyncHandler<DeleteWorkteamRequest,DeleteWorkteamResult> asyncHandler)
Deletes an existing work team. This operation can't be undone.
deleteWorkteamRequest - 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<DescribeAlgorithmResult> describeAlgorithmAsync(DescribeAlgorithmRequest describeAlgorithmRequest)
Returns a description of the specified algorithm that is in your account.
describeAlgorithmRequest - Future<DescribeAlgorithmResult> describeAlgorithmAsync(DescribeAlgorithmRequest describeAlgorithmRequest, AsyncHandler<DescribeAlgorithmRequest,DescribeAlgorithmResult> asyncHandler)
Returns a description of the specified algorithm that is in your account.
describeAlgorithmRequest - 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<DescribeAppResult> describeAppAsync(DescribeAppRequest describeAppRequest)
Describes the app.
describeAppRequest - Future<DescribeAppResult> describeAppAsync(DescribeAppRequest describeAppRequest, AsyncHandler<DescribeAppRequest,DescribeAppResult> asyncHandler)
Describes the app.
describeAppRequest - 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<DescribeAutoMLJobResult> describeAutoMLJobAsync(DescribeAutoMLJobRequest describeAutoMLJobRequest)
Returns information about an Amazon SageMaker job.
describeAutoMLJobRequest - Future<DescribeAutoMLJobResult> describeAutoMLJobAsync(DescribeAutoMLJobRequest describeAutoMLJobRequest, AsyncHandler<DescribeAutoMLJobRequest,DescribeAutoMLJobResult> asyncHandler)
Returns information about an Amazon SageMaker job.
describeAutoMLJobRequest - 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<DescribeCodeRepositoryResult> describeCodeRepositoryAsync(DescribeCodeRepositoryRequest describeCodeRepositoryRequest)
Gets details about the specified Git repository.
describeCodeRepositoryRequest - Future<DescribeCodeRepositoryResult> describeCodeRepositoryAsync(DescribeCodeRepositoryRequest describeCodeRepositoryRequest, AsyncHandler<DescribeCodeRepositoryRequest,DescribeCodeRepositoryResult> asyncHandler)
Gets details about the specified Git repository.
describeCodeRepositoryRequest - 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<DescribeCompilationJobResult> describeCompilationJobAsync(DescribeCompilationJobRequest describeCompilationJobRequest)
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
describeCompilationJobRequest - Future<DescribeCompilationJobResult> describeCompilationJobAsync(DescribeCompilationJobRequest describeCompilationJobRequest, AsyncHandler<DescribeCompilationJobRequest,DescribeCompilationJobResult> asyncHandler)
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
describeCompilationJobRequest - 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<DescribeDomainResult> describeDomainAsync(DescribeDomainRequest describeDomainRequest)
The description of the domain.
describeDomainRequest - Future<DescribeDomainResult> describeDomainAsync(DescribeDomainRequest describeDomainRequest, AsyncHandler<DescribeDomainRequest,DescribeDomainResult> asyncHandler)
The description of the domain.
describeDomainRequest - 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<DescribeExperimentResult> describeExperimentAsync(DescribeExperimentRequest describeExperimentRequest)
Provides a list of an experiment's properties.
describeExperimentRequest - Future<DescribeExperimentResult> describeExperimentAsync(DescribeExperimentRequest describeExperimentRequest, AsyncHandler<DescribeExperimentRequest,DescribeExperimentResult> asyncHandler)
Provides a list of an experiment's properties.
describeExperimentRequest - 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<DescribeFlowDefinitionResult> describeFlowDefinitionAsync(DescribeFlowDefinitionRequest describeFlowDefinitionRequest)
Returns information about the specified flow definition.
describeFlowDefinitionRequest - Future<DescribeFlowDefinitionResult> describeFlowDefinitionAsync(DescribeFlowDefinitionRequest describeFlowDefinitionRequest, AsyncHandler<DescribeFlowDefinitionRequest,DescribeFlowDefinitionResult> asyncHandler)
Returns information about the specified flow definition.
describeFlowDefinitionRequest - 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<DescribeHumanTaskUiResult> describeHumanTaskUiAsync(DescribeHumanTaskUiRequest describeHumanTaskUiRequest)
Returns information about the requested human task user interface (worker task template).
describeHumanTaskUiRequest - Future<DescribeHumanTaskUiResult> describeHumanTaskUiAsync(DescribeHumanTaskUiRequest describeHumanTaskUiRequest, AsyncHandler<DescribeHumanTaskUiRequest,DescribeHumanTaskUiResult> asyncHandler)
Returns information about the requested human task user interface (worker task template).
describeHumanTaskUiRequest - 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<DescribeLabelingJobResult> describeLabelingJobAsync(DescribeLabelingJobRequest describeLabelingJobRequest)
Gets information about a labeling job.
describeLabelingJobRequest - Future<DescribeLabelingJobResult> describeLabelingJobAsync(DescribeLabelingJobRequest describeLabelingJobRequest, AsyncHandler<DescribeLabelingJobRequest,DescribeLabelingJobResult> asyncHandler)
Gets information about a labeling job.
describeLabelingJobRequest - 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<DescribeModelPackageResult> describeModelPackageAsync(DescribeModelPackageRequest describeModelPackageRequest)
Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.
To create models in Amazon SageMaker, buyers can subscribe to model packages listed on AWS Marketplace.
describeModelPackageRequest - Future<DescribeModelPackageResult> describeModelPackageAsync(DescribeModelPackageRequest describeModelPackageRequest, AsyncHandler<DescribeModelPackageRequest,DescribeModelPackageResult> asyncHandler)
Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.
To create models in Amazon SageMaker, buyers can subscribe to model packages listed on AWS Marketplace.
describeModelPackageRequest - 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<DescribeMonitoringScheduleResult> describeMonitoringScheduleAsync(DescribeMonitoringScheduleRequest describeMonitoringScheduleRequest)
Describes the schedule for a monitoring job.
describeMonitoringScheduleRequest - Future<DescribeMonitoringScheduleResult> describeMonitoringScheduleAsync(DescribeMonitoringScheduleRequest describeMonitoringScheduleRequest, AsyncHandler<DescribeMonitoringScheduleRequest,DescribeMonitoringScheduleResult> asyncHandler)
Describes the schedule for a monitoring job.
describeMonitoringScheduleRequest - 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 Step 2.1: (Optional) Customize a Notebook Instance.
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 Step 2.1: (Optional) Customize a Notebook Instance.
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<DescribeProcessingJobResult> describeProcessingJobAsync(DescribeProcessingJobRequest describeProcessingJobRequest)
Returns a description of a processing job.
describeProcessingJobRequest - Future<DescribeProcessingJobResult> describeProcessingJobAsync(DescribeProcessingJobRequest describeProcessingJobRequest, AsyncHandler<DescribeProcessingJobRequest,DescribeProcessingJobResult> asyncHandler)
Returns a description of a processing job.
describeProcessingJobRequest - 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<DescribeSubscribedWorkteamResult> describeSubscribedWorkteamAsync(DescribeSubscribedWorkteamRequest describeSubscribedWorkteamRequest)
Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the AWS Marketplace.
describeSubscribedWorkteamRequest - Future<DescribeSubscribedWorkteamResult> describeSubscribedWorkteamAsync(DescribeSubscribedWorkteamRequest describeSubscribedWorkteamRequest, AsyncHandler<DescribeSubscribedWorkteamRequest,DescribeSubscribedWorkteamResult> asyncHandler)
Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the AWS Marketplace.
describeSubscribedWorkteamRequest - 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<DescribeTrialResult> describeTrialAsync(DescribeTrialRequest describeTrialRequest)
Provides a list of a trial's properties.
describeTrialRequest - Future<DescribeTrialResult> describeTrialAsync(DescribeTrialRequest describeTrialRequest, AsyncHandler<DescribeTrialRequest,DescribeTrialResult> asyncHandler)
Provides a list of a trial's properties.
describeTrialRequest - 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<DescribeTrialComponentResult> describeTrialComponentAsync(DescribeTrialComponentRequest describeTrialComponentRequest)
Provides a list of a trials component's properties.
describeTrialComponentRequest - Future<DescribeTrialComponentResult> describeTrialComponentAsync(DescribeTrialComponentRequest describeTrialComponentRequest, AsyncHandler<DescribeTrialComponentRequest,DescribeTrialComponentResult> asyncHandler)
Provides a list of a trials component's properties.
describeTrialComponentRequest - 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<DescribeUserProfileResult> describeUserProfileAsync(DescribeUserProfileRequest describeUserProfileRequest)
Describes a user profile. For more information, see CreateUserProfile.
describeUserProfileRequest - Future<DescribeUserProfileResult> describeUserProfileAsync(DescribeUserProfileRequest describeUserProfileRequest, AsyncHandler<DescribeUserProfileRequest,DescribeUserProfileResult> asyncHandler)
Describes a user profile. For more information, see CreateUserProfile.
describeUserProfileRequest - 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<DescribeWorkforceResult> describeWorkforceAsync(DescribeWorkforceRequest describeWorkforceRequest)
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks.
This operation applies only to private workforces.
describeWorkforceRequest - Future<DescribeWorkforceResult> describeWorkforceAsync(DescribeWorkforceRequest describeWorkforceRequest, AsyncHandler<DescribeWorkforceRequest,DescribeWorkforceResult> asyncHandler)
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks.
This operation applies only to private workforces.
describeWorkforceRequest - 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<DescribeWorkteamResult> describeWorkteamAsync(DescribeWorkteamRequest describeWorkteamRequest)
Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
describeWorkteamRequest - Future<DescribeWorkteamResult> describeWorkteamAsync(DescribeWorkteamRequest describeWorkteamRequest, AsyncHandler<DescribeWorkteamRequest,DescribeWorkteamResult> asyncHandler)
Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
describeWorkteamRequest - 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<DisassociateTrialComponentResult> disassociateTrialComponentAsync(DisassociateTrialComponentRequest disassociateTrialComponentRequest)
Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API.
To get a list of the trials a component is associated with, use the Search API. Specify
ExperimentTrialComponent for the Resource parameter. The list appears in the response
under Results.TrialComponent.Parents.
disassociateTrialComponentRequest - Future<DisassociateTrialComponentResult> disassociateTrialComponentAsync(DisassociateTrialComponentRequest disassociateTrialComponentRequest, AsyncHandler<DisassociateTrialComponentRequest,DisassociateTrialComponentResult> asyncHandler)
Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API.
To get a list of the trials a component is associated with, use the Search API. Specify
ExperimentTrialComponent for the Resource parameter. The list appears in the response
under Results.TrialComponent.Parents.
disassociateTrialComponentRequest - 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<GetSearchSuggestionsResult> getSearchSuggestionsAsync(GetSearchSuggestionsRequest getSearchSuggestionsRequest)
An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of
possible matches for the property name to use in Search queries. Provides suggestions for
HyperParameters, Tags, and Metrics.
getSearchSuggestionsRequest - Future<GetSearchSuggestionsResult> getSearchSuggestionsAsync(GetSearchSuggestionsRequest getSearchSuggestionsRequest, AsyncHandler<GetSearchSuggestionsRequest,GetSearchSuggestionsResult> asyncHandler)
An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of
possible matches for the property name to use in Search queries. Provides suggestions for
HyperParameters, Tags, and Metrics.
getSearchSuggestionsRequest - 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<ListAlgorithmsResult> listAlgorithmsAsync(ListAlgorithmsRequest listAlgorithmsRequest)
Lists the machine learning algorithms that have been created.
listAlgorithmsRequest - Future<ListAlgorithmsResult> listAlgorithmsAsync(ListAlgorithmsRequest listAlgorithmsRequest, AsyncHandler<ListAlgorithmsRequest,ListAlgorithmsResult> asyncHandler)
Lists the machine learning algorithms that have been created.
listAlgorithmsRequest - 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<ListAppsResult> listAppsAsync(ListAppsRequest listAppsRequest)
Lists apps.
listAppsRequest - Future<ListAppsResult> listAppsAsync(ListAppsRequest listAppsRequest, AsyncHandler<ListAppsRequest,ListAppsResult> asyncHandler)
Lists apps.
listAppsRequest - 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<ListAutoMLJobsResult> listAutoMLJobsAsync(ListAutoMLJobsRequest listAutoMLJobsRequest)
Request a list of jobs.
listAutoMLJobsRequest - Future<ListAutoMLJobsResult> listAutoMLJobsAsync(ListAutoMLJobsRequest listAutoMLJobsRequest, AsyncHandler<ListAutoMLJobsRequest,ListAutoMLJobsResult> asyncHandler)
Request a list of jobs.
listAutoMLJobsRequest - 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<ListCandidatesForAutoMLJobResult> listCandidatesForAutoMLJobAsync(ListCandidatesForAutoMLJobRequest listCandidatesForAutoMLJobRequest)
List the Candidates created for the job.
listCandidatesForAutoMLJobRequest - Future<ListCandidatesForAutoMLJobResult> listCandidatesForAutoMLJobAsync(ListCandidatesForAutoMLJobRequest listCandidatesForAutoMLJobRequest, AsyncHandler<ListCandidatesForAutoMLJobRequest,ListCandidatesForAutoMLJobResult> asyncHandler)
List the Candidates created for the job.
listCandidatesForAutoMLJobRequest - 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<ListCodeRepositoriesResult> listCodeRepositoriesAsync(ListCodeRepositoriesRequest listCodeRepositoriesRequest)
Gets a list of the Git repositories in your account.
listCodeRepositoriesRequest - Future<ListCodeRepositoriesResult> listCodeRepositoriesAsync(ListCodeRepositoriesRequest listCodeRepositoriesRequest, AsyncHandler<ListCodeRepositoriesRequest,ListCodeRepositoriesResult> asyncHandler)
Gets a list of the Git repositories in your account.
listCodeRepositoriesRequest - 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<ListCompilationJobsResult> listCompilationJobsAsync(ListCompilationJobsRequest listCompilationJobsRequest)
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
listCompilationJobsRequest - Future<ListCompilationJobsResult> listCompilationJobsAsync(ListCompilationJobsRequest listCompilationJobsRequest, AsyncHandler<ListCompilationJobsRequest,ListCompilationJobsResult> asyncHandler)
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
listCompilationJobsRequest - 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<ListDomainsResult> listDomainsAsync(ListDomainsRequest listDomainsRequest)
Lists the domains.
listDomainsRequest - Future<ListDomainsResult> listDomainsAsync(ListDomainsRequest listDomainsRequest, AsyncHandler<ListDomainsRequest,ListDomainsResult> asyncHandler)
Lists the domains.
listDomainsRequest - 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<ListExperimentsResult> listExperimentsAsync(ListExperimentsRequest listExperimentsRequest)
Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
listExperimentsRequest - Future<ListExperimentsResult> listExperimentsAsync(ListExperimentsRequest listExperimentsRequest, AsyncHandler<ListExperimentsRequest,ListExperimentsResult> asyncHandler)
Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
listExperimentsRequest - 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<ListFlowDefinitionsResult> listFlowDefinitionsAsync(ListFlowDefinitionsRequest listFlowDefinitionsRequest)
Returns information about the flow definitions in your account.
listFlowDefinitionsRequest - Future<ListFlowDefinitionsResult> listFlowDefinitionsAsync(ListFlowDefinitionsRequest listFlowDefinitionsRequest, AsyncHandler<ListFlowDefinitionsRequest,ListFlowDefinitionsResult> asyncHandler)
Returns information about the flow definitions in your account.
listFlowDefinitionsRequest - 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<ListHumanTaskUisResult> listHumanTaskUisAsync(ListHumanTaskUisRequest listHumanTaskUisRequest)
Returns information about the human task user interfaces in your account.
listHumanTaskUisRequest - Future<ListHumanTaskUisResult> listHumanTaskUisAsync(ListHumanTaskUisRequest listHumanTaskUisRequest, AsyncHandler<ListHumanTaskUisRequest,ListHumanTaskUisResult> asyncHandler)
Returns information about the human task user interfaces in your account.
listHumanTaskUisRequest - 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<ListLabelingJobsResult> listLabelingJobsAsync(ListLabelingJobsRequest listLabelingJobsRequest)
Gets a list of labeling jobs.
listLabelingJobsRequest - Future<ListLabelingJobsResult> listLabelingJobsAsync(ListLabelingJobsRequest listLabelingJobsRequest, AsyncHandler<ListLabelingJobsRequest,ListLabelingJobsResult> asyncHandler)
Gets a list of labeling jobs.
listLabelingJobsRequest - 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<ListLabelingJobsForWorkteamResult> listLabelingJobsForWorkteamAsync(ListLabelingJobsForWorkteamRequest listLabelingJobsForWorkteamRequest)
Gets a list of labeling jobs assigned to a specified work team.
listLabelingJobsForWorkteamRequest - Future<ListLabelingJobsForWorkteamResult> listLabelingJobsForWorkteamAsync(ListLabelingJobsForWorkteamRequest listLabelingJobsForWorkteamRequest, AsyncHandler<ListLabelingJobsForWorkteamRequest,ListLabelingJobsForWorkteamResult> asyncHandler)
Gets a list of labeling jobs assigned to a specified work team.
listLabelingJobsForWorkteamRequest - 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<ListModelPackagesResult> listModelPackagesAsync(ListModelPackagesRequest listModelPackagesRequest)
Lists the model packages that have been created.
listModelPackagesRequest - Future<ListModelPackagesResult> listModelPackagesAsync(ListModelPackagesRequest listModelPackagesRequest, AsyncHandler<ListModelPackagesRequest,ListModelPackagesResult> asyncHandler)
Lists the model packages that have been created.
listModelPackagesRequest - 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<ListMonitoringExecutionsResult> listMonitoringExecutionsAsync(ListMonitoringExecutionsRequest listMonitoringExecutionsRequest)
Returns list of all monitoring job executions.
listMonitoringExecutionsRequest - Future<ListMonitoringExecutionsResult> listMonitoringExecutionsAsync(ListMonitoringExecutionsRequest listMonitoringExecutionsRequest, AsyncHandler<ListMonitoringExecutionsRequest,ListMonitoringExecutionsResult> asyncHandler)
Returns list of all monitoring job executions.
listMonitoringExecutionsRequest - 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<ListMonitoringSchedulesResult> listMonitoringSchedulesAsync(ListMonitoringSchedulesRequest listMonitoringSchedulesRequest)
Returns list of all monitoring schedules.
listMonitoringSchedulesRequest - Future<ListMonitoringSchedulesResult> listMonitoringSchedulesAsync(ListMonitoringSchedulesRequest listMonitoringSchedulesRequest, AsyncHandler<ListMonitoringSchedulesRequest,ListMonitoringSchedulesResult> asyncHandler)
Returns list of all monitoring schedules.
listMonitoringSchedulesRequest - 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<ListProcessingJobsResult> listProcessingJobsAsync(ListProcessingJobsRequest listProcessingJobsRequest)
Lists processing jobs that satisfy various filters.
listProcessingJobsRequest - Future<ListProcessingJobsResult> listProcessingJobsAsync(ListProcessingJobsRequest listProcessingJobsRequest, AsyncHandler<ListProcessingJobsRequest,ListProcessingJobsResult> asyncHandler)
Lists processing jobs that satisfy various filters.
listProcessingJobsRequest - 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<ListSubscribedWorkteamsResult> listSubscribedWorkteamsAsync(ListSubscribedWorkteamsRequest listSubscribedWorkteamsRequest)
Gets a list of the work teams that you are subscribed to in the AWS Marketplace. The list may be empty if no work
team satisfies the filter specified in the NameContains parameter.
listSubscribedWorkteamsRequest - Future<ListSubscribedWorkteamsResult> listSubscribedWorkteamsAsync(ListSubscribedWorkteamsRequest listSubscribedWorkteamsRequest, AsyncHandler<ListSubscribedWorkteamsRequest,ListSubscribedWorkteamsResult> asyncHandler)
Gets a list of the work teams that you are subscribed to in the AWS Marketplace. The list may be empty if no work
team satisfies the filter specified in the NameContains parameter.
listSubscribedWorkteamsRequest - 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<ListTrialComponentsResult> listTrialComponentsAsync(ListTrialComponentsRequest listTrialComponentsRequest)
Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:
ExperimentName
SourceArn
TrialName
listTrialComponentsRequest - Future<ListTrialComponentsResult> listTrialComponentsAsync(ListTrialComponentsRequest listTrialComponentsRequest, AsyncHandler<ListTrialComponentsRequest,ListTrialComponentsResult> asyncHandler)
Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:
ExperimentName
SourceArn
TrialName
listTrialComponentsRequest - 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<ListTrialsResult> listTrialsAsync(ListTrialsRequest listTrialsRequest)
Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
listTrialsRequest - Future<ListTrialsResult> listTrialsAsync(ListTrialsRequest listTrialsRequest, AsyncHandler<ListTrialsRequest,ListTrialsResult> asyncHandler)
Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
listTrialsRequest - 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<ListUserProfilesResult> listUserProfilesAsync(ListUserProfilesRequest listUserProfilesRequest)
Lists user profiles.
listUserProfilesRequest - Future<ListUserProfilesResult> listUserProfilesAsync(ListUserProfilesRequest listUserProfilesRequest, AsyncHandler<ListUserProfilesRequest,ListUserProfilesResult> asyncHandler)
Lists user profiles.
listUserProfilesRequest - 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<ListWorkteamsResult> listWorkteamsAsync(ListWorkteamsRequest listWorkteamsRequest)
Gets a list of work teams that you have defined in a region. The list may be empty if no work team satisfies the
filter specified in the NameContains parameter.
listWorkteamsRequest - Future<ListWorkteamsResult> listWorkteamsAsync(ListWorkteamsRequest listWorkteamsRequest, AsyncHandler<ListWorkteamsRequest,ListWorkteamsResult> asyncHandler)
Gets a list of work teams that you have defined in a region. The list may be empty if no work team satisfies the
filter specified in the NameContains parameter.
listWorkteamsRequest - 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<RenderUiTemplateResult> renderUiTemplateAsync(RenderUiTemplateRequest renderUiTemplateRequest)
Renders the UI template so that you can preview the worker's experience.
renderUiTemplateRequest - Future<RenderUiTemplateResult> renderUiTemplateAsync(RenderUiTemplateRequest renderUiTemplateRequest, AsyncHandler<RenderUiTemplateRequest,RenderUiTemplateResult> asyncHandler)
Renders the UI template so that you can preview the worker's experience.
renderUiTemplateRequest - 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<SearchResult> searchAsync(SearchRequest searchRequest)
Finds Amazon SageMaker resources that match a search query. Matching resources are returned as a list of
SearchRecord objects in the response. You can sort the search results by any resource property in a
ascending or descending order.
You can query against the following value types: numeric, text, Boolean, and timestamp.
searchRequest - Future<SearchResult> searchAsync(SearchRequest searchRequest, AsyncHandler<SearchRequest,SearchResult> asyncHandler)
Finds Amazon SageMaker resources that match a search query. Matching resources are returned as a list of
SearchRecord objects in the response. You can sort the search results by any resource property in a
ascending or descending order.
You can query against the following value types: numeric, text, Boolean, and timestamp.
searchRequest - 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<StartMonitoringScheduleResult> startMonitoringScheduleAsync(StartMonitoringScheduleRequest startMonitoringScheduleRequest)
Starts a previously stopped monitoring schedule.
New monitoring schedules are immediately started after creation.
startMonitoringScheduleRequest - Future<StartMonitoringScheduleResult> startMonitoringScheduleAsync(StartMonitoringScheduleRequest startMonitoringScheduleRequest, AsyncHandler<StartMonitoringScheduleRequest,StartMonitoringScheduleResult> asyncHandler)
Starts a previously stopped monitoring schedule.
New monitoring schedules are immediately started after creation.
startMonitoringScheduleRequest - 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<StopAutoMLJobResult> stopAutoMLJobAsync(StopAutoMLJobRequest stopAutoMLJobRequest)
A method for forcing the termination of a running job.
stopAutoMLJobRequest - Future<StopAutoMLJobResult> stopAutoMLJobAsync(StopAutoMLJobRequest stopAutoMLJobRequest, AsyncHandler<StopAutoMLJobRequest,StopAutoMLJobResult> asyncHandler)
A method for forcing the termination of a running job.
stopAutoMLJobRequest - 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<StopCompilationJobResult> stopCompilationJobAsync(StopCompilationJobRequest stopCompilationJobRequest)
Stops a model compilation job.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal.
When it receives a StopCompilationJob request, Amazon SageMaker changes the
CompilationJobSummary$CompilationJobStatus of the job to Stopping. After Amazon SageMaker
stops the job, it sets the CompilationJobSummary$CompilationJobStatus to Stopped.
stopCompilationJobRequest - Future<StopCompilationJobResult> stopCompilationJobAsync(StopCompilationJobRequest stopCompilationJobRequest, AsyncHandler<StopCompilationJobRequest,StopCompilationJobResult> asyncHandler)
Stops a model compilation job.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal.
When it receives a StopCompilationJob request, Amazon SageMaker changes the
CompilationJobSummary$CompilationJobStatus of the job to Stopping. After Amazon SageMaker
stops the job, it sets the CompilationJobSummary$CompilationJobStatus to Stopped.
stopCompilationJobRequest - 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<StopLabelingJobResult> stopLabelingJobAsync(StopLabelingJobRequest stopLabelingJobRequest)
Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.
stopLabelingJobRequest - Future<StopLabelingJobResult> stopLabelingJobAsync(StopLabelingJobRequest stopLabelingJobRequest, AsyncHandler<StopLabelingJobRequest,StopLabelingJobResult> asyncHandler)
Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.
stopLabelingJobRequest - 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<StopMonitoringScheduleResult> stopMonitoringScheduleAsync(StopMonitoringScheduleRequest stopMonitoringScheduleRequest)
Stops a previously started monitoring schedule.
stopMonitoringScheduleRequest - Future<StopMonitoringScheduleResult> stopMonitoringScheduleAsync(StopMonitoringScheduleRequest stopMonitoringScheduleRequest, AsyncHandler<StopMonitoringScheduleRequest,StopMonitoringScheduleResult> asyncHandler)
Stops a previously started monitoring schedule.
stopMonitoringScheduleRequest - 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. Amazon SageMaker stops charging you for the ML
compute instance when you call StopNotebookInstance.
To access data on the ML storage volume for a notebook instance that has been terminated, call the
StartNotebookInstance API. StartNotebookInstance launches another ML compute instance,
configures it, and attaches the preserved ML storage volume so you can continue your work.
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. Amazon SageMaker stops charging you for the ML
compute instance when you call StopNotebookInstance.
To access data on the ML storage volume for a notebook instance that has been terminated, call the
StartNotebookInstance API. StartNotebookInstance launches another ML compute instance,
configures it, and attaches the preserved ML storage volume so you can continue your work.
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<StopProcessingJobResult> stopProcessingJobAsync(StopProcessingJobRequest stopProcessingJobRequest)
Stops a processing job.
stopProcessingJobRequest - Future<StopProcessingJobResult> stopProcessingJobAsync(StopProcessingJobRequest stopProcessingJobRequest, AsyncHandler<StopProcessingJobRequest,StopProcessingJobResult> asyncHandler)
Stops a processing job.
stopProcessingJobRequest - 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.
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.
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<UpdateCodeRepositoryResult> updateCodeRepositoryAsync(UpdateCodeRepositoryRequest updateCodeRepositoryRequest)
Updates the specified Git repository with the specified values.
updateCodeRepositoryRequest - Future<UpdateCodeRepositoryResult> updateCodeRepositoryAsync(UpdateCodeRepositoryRequest updateCodeRepositoryRequest, AsyncHandler<UpdateCodeRepositoryRequest,UpdateCodeRepositoryResult> asyncHandler)
Updates the specified Git repository with the specified values.
updateCodeRepositoryRequest - 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<UpdateDomainResult> updateDomainAsync(UpdateDomainRequest updateDomainRequest)
Updates the default settings for new user profiles in the domain.
updateDomainRequest - Future<UpdateDomainResult> updateDomainAsync(UpdateDomainRequest updateDomainRequest, AsyncHandler<UpdateDomainRequest,UpdateDomainResult> asyncHandler)
Updates the default settings for new user profiles in the domain.
updateDomainRequest - 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 must not delete an EndpointConfig in use by an endpoint that is live or while the
UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To
update an endpoint, you must create a new EndpointConfig.
If you delete the EndpointConfig of an endpoint that is active or being created or updated you may
lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop
incurring charges.
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 must not delete an EndpointConfig in use by an endpoint that is live or while the
UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To
update an endpoint, you must create a new EndpointConfig.
If you delete the EndpointConfig of an endpoint that is active or being created or updated you may
lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop
incurring charges.
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<UpdateExperimentResult> updateExperimentAsync(UpdateExperimentRequest updateExperimentRequest)
Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
updateExperimentRequest - Future<UpdateExperimentResult> updateExperimentAsync(UpdateExperimentRequest updateExperimentRequest, AsyncHandler<UpdateExperimentRequest,UpdateExperimentResult> asyncHandler)
Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
updateExperimentRequest - 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<UpdateMonitoringScheduleResult> updateMonitoringScheduleAsync(UpdateMonitoringScheduleRequest updateMonitoringScheduleRequest)
Updates a previously created schedule.
updateMonitoringScheduleRequest - Future<UpdateMonitoringScheduleResult> updateMonitoringScheduleAsync(UpdateMonitoringScheduleRequest updateMonitoringScheduleRequest, AsyncHandler<UpdateMonitoringScheduleRequest,UpdateMonitoringScheduleResult> asyncHandler)
Updates a previously created schedule.
updateMonitoringScheduleRequest - 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.
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.
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.Future<UpdateTrialResult> updateTrialAsync(UpdateTrialRequest updateTrialRequest)
Updates the display name of a trial.
updateTrialRequest - Future<UpdateTrialResult> updateTrialAsync(UpdateTrialRequest updateTrialRequest, AsyncHandler<UpdateTrialRequest,UpdateTrialResult> asyncHandler)
Updates the display name of a trial.
updateTrialRequest - 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<UpdateTrialComponentResult> updateTrialComponentAsync(UpdateTrialComponentRequest updateTrialComponentRequest)
Updates one or more properties of a trial component.
updateTrialComponentRequest - Future<UpdateTrialComponentResult> updateTrialComponentAsync(UpdateTrialComponentRequest updateTrialComponentRequest, AsyncHandler<UpdateTrialComponentRequest,UpdateTrialComponentResult> asyncHandler)
Updates one or more properties of a trial component.
updateTrialComponentRequest - 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<UpdateUserProfileResult> updateUserProfileAsync(UpdateUserProfileRequest updateUserProfileRequest)
Updates a user profile.
updateUserProfileRequest - Future<UpdateUserProfileResult> updateUserProfileAsync(UpdateUserProfileRequest updateUserProfileRequest, AsyncHandler<UpdateUserProfileRequest,UpdateUserProfileResult> asyncHandler)
Updates a user profile.
updateUserProfileRequest - 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<UpdateWorkforceResult> updateWorkforceAsync(UpdateWorkforceRequest updateWorkforceRequest)
Restricts access to tasks assigned to workers in the specified workforce to those within specific ranges of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs.
By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses,
workers who attempt to access tasks using any IP address outside the specified range are denied access and get a
Not Found error message on the worker portal. After restricting access with this operation, you can
see the allowed IP values for a private workforce with the operation.
This operation applies only to private workforces.
updateWorkforceRequest - Future<UpdateWorkforceResult> updateWorkforceAsync(UpdateWorkforceRequest updateWorkforceRequest, AsyncHandler<UpdateWorkforceRequest,UpdateWorkforceResult> asyncHandler)
Restricts access to tasks assigned to workers in the specified workforce to those within specific ranges of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs.
By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses,
workers who attempt to access tasks using any IP address outside the specified range are denied access and get a
Not Found error message on the worker portal. After restricting access with this operation, you can
see the allowed IP values for a private workforce with the operation.
This operation applies only to private workforces.
updateWorkforceRequest - 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<UpdateWorkteamResult> updateWorkteamAsync(UpdateWorkteamRequest updateWorkteamRequest)
Updates an existing work team with new member definitions or description.
updateWorkteamRequest - Future<UpdateWorkteamResult> updateWorkteamAsync(UpdateWorkteamRequest updateWorkteamRequest, AsyncHandler<UpdateWorkteamRequest,UpdateWorkteamResult> asyncHandler)
Updates an existing work team with new member definitions or description.
updateWorkteamRequest - 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.