cdk-stepfunctions-patterns/node_modules/aws-sdk/clients/sagemaker.d.ts

A set of Step Functions high-level patterns.

import {Request} from '../lib/request';
import {Response} from '../lib/response';
import {AWSError} from '../lib/error';
import {Service} from '../lib/service';
import {WaiterConfiguration} from '../lib/service';
import {ServiceConfigurationOptions} from '../lib/service';
import {ConfigBase as Config} from '../lib/config-base';
interface Blob {}
declare class SageMaker extends Service {
  /**
   * Constructs a service object. This object has one method for each API operation.
   */
  constructor(options?: SageMaker.Types.ClientConfiguration)
  config: Config & SageMaker.Types.ClientConfiguration;
  /**
   * 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  
   */
  addTags(params: SageMaker.Types.AddTagsInput, callback?: (err: AWSError, data: SageMaker.Types.AddTagsOutput) => void): Request<SageMaker.Types.AddTagsOutput, AWSError>;
  /**
   * 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  
   */
  addTags(callback?: (err: AWSError, data: SageMaker.Types.AddTagsOutput) => void): Request<SageMaker.Types.AddTagsOutput, AWSError>;
  /**
   * 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.
   */
  associateTrialComponent(params: SageMaker.Types.AssociateTrialComponentRequest, callback?: (err: AWSError, data: SageMaker.Types.AssociateTrialComponentResponse) => void): Request<SageMaker.Types.AssociateTrialComponentResponse, AWSError>;
  /**
   * 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.
   */
  associateTrialComponent(callback?: (err: AWSError, data: SageMaker.Types.AssociateTrialComponentResponse) => void): Request<SageMaker.Types.AssociateTrialComponentResponse, AWSError>;
  /**
   * Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
   */
  createAlgorithm(params: SageMaker.Types.CreateAlgorithmInput, callback?: (err: AWSError, data: SageMaker.Types.CreateAlgorithmOutput) => void): Request<SageMaker.Types.CreateAlgorithmOutput, AWSError>;
  /**
   * Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
   */
  createAlgorithm(callback?: (err: AWSError, data: SageMaker.Types.CreateAlgorithmOutput) => void): Request<SageMaker.Types.CreateAlgorithmOutput, AWSError>;
  /**
   * Creates a running App for the specified UserProfile. Supported Apps are JupyterServer and KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
   */
  createApp(params: SageMaker.Types.CreateAppRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateAppResponse) => void): Request<SageMaker.Types.CreateAppResponse, AWSError>;
  /**
   * Creates a running App for the specified UserProfile. Supported Apps are JupyterServer and KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
   */
  createApp(callback?: (err: AWSError, data: SageMaker.Types.CreateAppResponse) => void): Request<SageMaker.Types.CreateAppResponse, AWSError>;
  /**
   * Creates an Autopilot job. Find the best performing model after you run an Autopilot job by calling . 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  Automate Model Development with Amazon SageMaker Autopilot.
   */
  createAutoMLJob(params: SageMaker.Types.CreateAutoMLJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateAutoMLJobResponse) => void): Request<SageMaker.Types.CreateAutoMLJobResponse, AWSError>;
  /**
   * Creates an Autopilot job. Find the best performing model after you run an Autopilot job by calling . 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  Automate Model Development with Amazon SageMaker Autopilot.
   */
  createAutoMLJob(callback?: (err: AWSError, data: SageMaker.Types.CreateAutoMLJobResponse) => void): Request<SageMaker.Types.CreateAutoMLJobResponse, AWSError>;
  /**
   * 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.
   */
  createCodeRepository(params: SageMaker.Types.CreateCodeRepositoryInput, callback?: (err: AWSError, data: SageMaker.Types.CreateCodeRepositoryOutput) => void): Request<SageMaker.Types.CreateCodeRepositoryOutput, AWSError>;
  /**
   * 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.
   */
  createCodeRepository(callback?: (err: AWSError, data: SageMaker.Types.CreateCodeRepositoryOutput) => void): Request<SageMaker.Types.CreateCodeRepositoryOutput, AWSError>;
  /**
   * 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.
   */
  createCompilationJob(params: SageMaker.Types.CreateCompilationJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateCompilationJobResponse) => void): Request<SageMaker.Types.CreateCompilationJobResponse, AWSError>;
  /**
   * 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.
   */
  createCompilationJob(callback?: (err: AWSError, data: SageMaker.Types.CreateCompilationJobResponse) => void): Request<SageMaker.Types.CreateCompilationJobResponse, AWSError>;
  /**
   * 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. 
   */
  createDomain(params: SageMaker.Types.CreateDomainRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateDomainResponse) => void): Request<SageMaker.Types.CreateDomainResponse, AWSError>;
  /**
   * 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. 
   */
  createDomain(callback?: (err: AWSError, data: SageMaker.Types.CreateDomainResponse) => void): Request<SageMaker.Types.CreateDomainResponse, AWSError>;
  /**
   * 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.
   */
  createEndpoint(params: SageMaker.Types.CreateEndpointInput, callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointOutput) => void): Request<SageMaker.Types.CreateEndpointOutput, AWSError>;
  /**
   * 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.
   */
  createEndpoint(callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointOutput) => void): Request<SageMaker.Types.CreateEndpointOutput, AWSError>;
  /**
   * Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.   Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.   In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.  If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.  For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).   When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting  Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read. 
   */
  createEndpointConfig(params: SageMaker.Types.CreateEndpointConfigInput, callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointConfigOutput) => void): Request<SageMaker.Types.CreateEndpointConfigOutput, AWSError>;
  /**
   * Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.   Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.   In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.  If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.  For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).   When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting  Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read. 
   */
  createEndpointConfig(callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointConfigOutput) => void): Request<SageMaker.Types.CreateEndpointConfigOutput, AWSError>;
  /**
   * Creates an SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model. The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant. When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to experiments, trials, trial components and then use the Search API to search for the tags. To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API. To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
   */
  createExperiment(params: SageMaker.Types.CreateExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateExperimentResponse) => void): Request<SageMaker.Types.CreateExperimentResponse, AWSError>;
  /**
   * Creates an SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model. The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant. When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to experiments, trials, trial components and then use the Search API to search for the tags. To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API. To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
   */
  createExperiment(callback?: (err: AWSError, data: SageMaker.Types.CreateExperimentResponse) => void): Request<SageMaker.Types.CreateExperimentResponse, AWSError>;
  /**
   * Creates a flow definition.
   */
  createFlowDefinition(params: SageMaker.Types.CreateFlowDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateFlowDefinitionResponse) => void): Request<SageMaker.Types.CreateFlowDefinitionResponse, AWSError>;
  /**
   * Creates a flow definition.
   */
  createFlowDefinition(callback?: (err: AWSError, data: SageMaker.Types.CreateFlowDefinitionResponse) => void): Request<SageMaker.Types.CreateFlowDefinitionResponse, AWSError>;
  /**
   * 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.
   */
  createHumanTaskUi(params: SageMaker.Types.CreateHumanTaskUiRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateHumanTaskUiResponse) => void): Request<SageMaker.Types.CreateHumanTaskUiResponse, AWSError>;
  /**
   * 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.
   */
  createHumanTaskUi(callback?: (err: AWSError, data: SageMaker.Types.CreateHumanTaskUiResponse) => void): Request<SageMaker.Types.CreateHumanTaskUiResponse, AWSError>;
  /**
   * 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.
   */
  createHyperParameterTuningJob(params: SageMaker.Types.CreateHyperParameterTuningJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateHyperParameterTuningJobResponse) => void): Request<SageMaker.Types.CreateHyperParameterTuningJobResponse, AWSError>;
  /**
   * 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.
   */
  createHyperParameterTuningJob(callback?: (err: AWSError, data: SageMaker.Types.CreateHyperParameterTuningJobResponse) => void): Request<SageMaker.Types.CreateHyperParameterTuningJobResponse, AWSError>;
  /**
   * 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.
   */
  createLabelingJob(params: SageMaker.Types.CreateLabelingJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateLabelingJobResponse) => void): Request<SageMaker.Types.CreateLabelingJobResponse, AWSError>;
  /**
   * 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.
   */
  createLabelingJob(callback?: (err: AWSError, data: SageMaker.Types.CreateLabelingJobResponse) => void): Request<SageMaker.Types.CreateLabelingJobResponse, AWSError>;
  /**
   * 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.
   */
  createModel(params: SageMaker.Types.CreateModelInput, callback?: (err: AWSError, data: SageMaker.Types.CreateModelOutput) => void): Request<SageMaker.Types.CreateModelOutput, AWSError>;
  /**
   * 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.
   */
  createModel(callback?: (err: AWSError, data: SageMaker.Types.CreateModelOutput) => void): Request<SageMaker.Types.CreateModelOutput, AWSError>;
  /**
   * 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.
   */
  createModelPackage(params: SageMaker.Types.CreateModelPackageInput, callback?: (err: AWSError, data: SageMaker.Types.CreateModelPackageOutput) => void): Request<SageMaker.Types.CreateModelPackageOutput, AWSError>;
  /**
   * 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.
   */
  createModelPackage(callback?: (err: AWSError, data: SageMaker.Types.CreateModelPackageOutput) => void): Request<SageMaker.Types.CreateModelPackageOutput, AWSError>;
  /**
   * Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
   */
  createMonitoringSchedule(params: SageMaker.Types.CreateMonitoringScheduleRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateMonitoringScheduleResponse) => void): Request<SageMaker.Types.CreateMonitoringScheduleResponse, AWSError>;
  /**
   * Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
   */
  createMonitoringSchedule(callback?: (err: AWSError, data: SageMaker.Types.CreateMonitoringScheduleResponse) => void): Request<SageMaker.Types.CreateMonitoringScheduleResponse, AWSError>;
  /**
   * 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. 
   */
  createNotebookInstance(params: SageMaker.Types.CreateNotebookInstanceInput, callback?: (err: AWSError, data: SageMaker.Types.CreateNotebookInstanceOutput) => void): Request<SageMaker.Types.CreateNotebookInstanceOutput, AWSError>;
  /**
   * 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. 
   */
  createNotebookInstance(callback?: (err: AWSError, data: SageMaker.Types.CreateNotebookInstanceOutput) => void): Request<SageMaker.Types.CreateNotebookInstanceOutput, AWSError>;
  /**
   * 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.
   */
  createNotebookInstanceLifecycleConfig(params: SageMaker.Types.CreateNotebookInstanceLifecycleConfigInput, callback?: (err: AWSError, data: SageMaker.Types.CreateNotebookInstanceLifecycleConfigOutput) => void): Request<SageMaker.Types.CreateNotebookInstanceLifecycleConfigOutput, AWSError>;
  /**
   * 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.
   */
  createNotebookInstanceLifecycleConfig(callback?: (err: AWSError, data: SageMaker.Types.CreateNotebookInstanceLifecycleConfigOutput) => void): Request<SageMaker.Types.CreateNotebookInstanceLifecycleConfigOutput, AWSError>;
  /**
   * 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. 
   */
  createPresignedDomainUrl(params: SageMaker.Types.CreatePresignedDomainUrlRequest, callback?: (err: AWSError, data: SageMaker.Types.CreatePresignedDomainUrlResponse) => void): Request<SageMaker.Types.CreatePresignedDomainUrlResponse, AWSError>;
  /**
   * 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. 
   */
  createPresignedDomainUrl(callback?: (err: AWSError, data: SageMaker.Types.CreatePresignedDomainUrlResponse) => void): Request<SageMaker.Types.CreatePresignedDomainUrlResponse, AWSError>;
  /**
   * Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.  The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance. You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.  The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page. 
   */
  createPresignedNotebookInstanceUrl(params: SageMaker.Types.CreatePresignedNotebookInstanceUrlInput, callback?: (err: AWSError, data: SageMaker.Types.CreatePresignedNotebookInstanceUrlOutput) => void): Request<SageMaker.Types.CreatePresignedNotebookInstanceUrlOutput, AWSError>;
  /**
   * Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.  The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance. You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.  The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page. 
   */
  createPresignedNotebookInstanceUrl(callback?: (err: AWSError, data: SageMaker.Types.CreatePresignedNotebookInstanceUrlOutput) => void): Request<SageMaker.Types.CreatePresignedNotebookInstanceUrlOutput, AWSError>;
  /**
   * Creates a processing job.
   */
  createProcessingJob(params: SageMaker.Types.CreateProcessingJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateProcessingJobResponse) => void): Request<SageMaker.Types.CreateProcessingJobResponse, AWSError>;
  /**
   * Creates a processing job.
   */
  createProcessingJob(callback?: (err: AWSError, data: SageMaker.Types.CreateProcessingJobResponse) => void): Request<SageMaker.Types.CreateProcessingJobResponse, AWSError>;
  /**
   * 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