AWS SDK Version 3 for .NET
API Reference

AWS services or capabilities described in AWS Documentation may vary by region/location. Click Getting Started with Amazon AWS to see specific differences applicable to the China (Beijing) Region.

Implementation for accessing SageMaker Provides APIs for creating and managing SageMaker resources.

Other Resources:

Inheritance Hierarchy

System.Object
  Amazon.Runtime.AmazonServiceClient
    Amazon.SageMaker.AmazonSageMakerClient

Namespace: Amazon.SageMaker
Assembly: AWSSDK.SageMaker.dll
Version: 3.x.y.z

Syntax

C#
public class AmazonSageMakerClient : AmazonServiceClient
         IAmazonSageMaker, IAmazonService, IDisposable

The AmazonSageMakerClient type exposes the following members

Constructors

NameDescription
Public Method AmazonSageMakerClient()

Constructs AmazonSageMakerClient with the credentials loaded from the application's default configuration, and if unsuccessful from the Instance Profile service on an EC2 instance. Example App.config with credentials set.

<?xml version="1.0" encoding="utf-8" ?>
<configuration>
    <appSettings>
        <add key="AWSProfileName" value="AWS Default"/>
    </appSettings>
</configuration>
             

Public Method AmazonSageMakerClient(RegionEndpoint)

Constructs AmazonSageMakerClient with the credentials loaded from the application's default configuration, and if unsuccessful from the Instance Profile service on an EC2 instance. Example App.config with credentials set.

<?xml version="1.0" encoding="utf-8" ?>
<configuration>
    <appSettings>
        <add key="AWSProfileName" value="AWS Default"/>
    </appSettings>
</configuration>
             

Public Method AmazonSageMakerClient(AmazonSageMakerConfig)

Constructs AmazonSageMakerClient with the credentials loaded from the application's default configuration, and if unsuccessful from the Instance Profile service on an EC2 instance. Example App.config with credentials set.

<?xml version="1.0" encoding="utf-8" ?>
<configuration>
    <appSettings>
        <add key="AWSProfileName" value="AWS Default"/>
    </appSettings>
</configuration>
             

Public Method AmazonSageMakerClient(AWSCredentials)

Constructs AmazonSageMakerClient with AWS Credentials

Public Method AmazonSageMakerClient(AWSCredentials, RegionEndpoint)

Constructs AmazonSageMakerClient with AWS Credentials

Public Method AmazonSageMakerClient(AWSCredentials, AmazonSageMakerConfig)

Constructs AmazonSageMakerClient with AWS Credentials and an AmazonSageMakerClient Configuration object.

Public Method AmazonSageMakerClient(string, string)

Constructs AmazonSageMakerClient with AWS Access Key ID and AWS Secret Key

Public Method AmazonSageMakerClient(string, string, RegionEndpoint)

Constructs AmazonSageMakerClient with AWS Access Key ID and AWS Secret Key

Public Method AmazonSageMakerClient(string, string, AmazonSageMakerConfig)

Constructs AmazonSageMakerClient with AWS Access Key ID, AWS Secret Key and an AmazonSageMakerClient Configuration object.

Public Method AmazonSageMakerClient(string, string, string)

Constructs AmazonSageMakerClient with AWS Access Key ID and AWS Secret Key

Public Method AmazonSageMakerClient(string, string, string, RegionEndpoint)

Constructs AmazonSageMakerClient with AWS Access Key ID and AWS Secret Key

Public Method AmazonSageMakerClient(string, string, string, AmazonSageMakerConfig)

Constructs AmazonSageMakerClient with AWS Access Key ID, AWS Secret Key and an AmazonSageMakerClient Configuration object.

Properties

NameTypeDescription
Public Property Config Amazon.Runtime.IClientConfig Inherited from Amazon.Runtime.AmazonServiceClient.
Public Property Paginators Amazon.SageMaker.Model.ISageMakerPaginatorFactory

Paginators for the service

Methods

Note:

Asynchronous operations (methods ending with Async) in the table below are for .NET 4.5 or higher. For .NET 3.5 the SDK follows the standard naming convention of BeginMethodName and EndMethodName to indicate asynchronous operations - these method pairs are not shown in the table below.

NameDescription
Public Method AddAssociation(AddAssociationRequest)

Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.

Public Method AddAssociationAsync(AddAssociationRequest, CancellationToken)

Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.

Public Method AddTags(AddTagsRequest)

Adds or overwrites one or more tags for the specified 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 Amazon Web Services 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

Tags that you add to a SageMaker Studio Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.

Public Method AddTagsAsync(AddTagsRequest, CancellationToken)

Adds or overwrites one or more tags for the specified 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 Amazon Web Services 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

Tags that you add to a SageMaker Studio Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.

Public Method AssociateTrialComponent(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.

Public Method AssociateTrialComponentAsync(AssociateTrialComponentRequest, CancellationToken)

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.

Public Method BatchDescribeModelPackage(BatchDescribeModelPackageRequest)

This action batch describes a list of versioned model packages

Public Method BatchDescribeModelPackageAsync(BatchDescribeModelPackageRequest, CancellationToken)

This action batch describes a list of versioned model packages

Public Method CreateAction(CreateActionRequest)

Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.

Public Method CreateActionAsync(CreateActionRequest, CancellationToken)

Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.

Public Method CreateAlgorithm(CreateAlgorithmRequest)

Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.

Public Method CreateAlgorithmAsync(CreateAlgorithmRequest, CancellationToken)

Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.

Public Method CreateApp(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.

Public Method CreateAppAsync(CreateAppRequest, CancellationToken)

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.

Public Method CreateAppImageConfig(CreateAppImageConfigRequest)

Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.

Public Method CreateAppImageConfigAsync(CreateAppImageConfigRequest, CancellationToken)

Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.

Public Method CreateArtifact(CreateArtifactRequest)

Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.

Public Method CreateArtifactAsync(CreateArtifactRequest, CancellationToken)

Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.

Public Method CreateAutoMLJob(CreateAutoMLJobRequest)

Creates an Autopilot job.

Find the best-performing model after you run an Autopilot job by calling .

For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.

Public Method CreateAutoMLJobAsync(CreateAutoMLJobRequest, CancellationToken)

Creates an Autopilot job.

Find the best-performing model after you run an Autopilot job by calling .

For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.

Public Method CreateCodeRepository(CreateCodeRepositoryRequest)

Creates a Git repository as a resource in your 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 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 Amazon Web Services CodeCommit or in any other Git repository.

Public Method CreateCodeRepositoryAsync(CreateCodeRepositoryRequest, CancellationToken)

Creates a Git repository as a resource in your 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 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 Amazon Web Services CodeCommit or in any other Git repository.

Public Method CreateCompilationJob(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 Amazon Web Services 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.

Public Method CreateCompilationJobAsync(CreateCompilationJobRequest, CancellationToken)

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 Amazon Web Services 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.

Public Method CreateContext(CreateContextRequest)

Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.

Public Method CreateContextAsync(CreateContextRequest, CancellationToken)

Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.

Public Method CreateDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest)

Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

Public Method CreateDataQualityJobDefinitionAsync(CreateDataQualityJobDefinitionRequest, CancellationToken)

Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

Public Method CreateDeviceFleet(CreateDeviceFleetRequest)

Creates a device fleet.

Public Method CreateDeviceFleetAsync(CreateDeviceFleetRequest, CancellationToken)

Creates a device fleet.

Public Method CreateDomain(CreateDomainRequest)

Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An Amazon Web Services account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.

EFS storage

When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.

SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.

VPC configuration

All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to Studio. The following options are available:

  • PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.

  • VpcOnly - All Studio traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.

    When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.

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.

For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.

Public Method CreateDomainAsync(CreateDomainRequest, CancellationToken)

Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An Amazon Web Services account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.

EFS storage

When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.

SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.

VPC configuration

All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to Studio. The following options are available:

  • PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.

  • VpcOnly - All Studio traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.

    When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.

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.

For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.

Public Method CreateEdgePackagingJob(CreateEdgePackagingJobRequest)

Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.

Public Method CreateEdgePackagingJobAsync(CreateEdgePackagingJobRequest, CancellationToken)

Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.

Public Method CreateEndpoint(CreateEndpointRequest)

Creates an endpoint using the endpoint configuration specified in the request. 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 SageMaker hosting services.

For an example that calls this method when deploying a model to SageMaker hosting services, see the Create Endpoint example notebook.

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 Amazon Web Services Region in your Amazon Web Services account.

When it receives the request, 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 SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. 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, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.

To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.

  • Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.

  • Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:

    "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]

    "Resource": [

    "arn:aws:sagemaker:region:account-id:endpoint/endpointName"

    "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"

    ]

    For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.

Public Method CreateEndpointAsync(CreateEndpointRequest, CancellationToken)

Creates an endpoint using the endpoint configuration specified in the request. 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 SageMaker hosting services.

For an example that calls this method when deploying a model to SageMaker hosting services, see the Create Endpoint example notebook.

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 Amazon Web Services Region in your Amazon Web Services account.

When it receives the request, 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 SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. 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, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.

To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.

  • Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.

  • Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:

    "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]

    "Resource": [

    "arn:aws:sagemaker:region:account-id:endpoint/endpointName"

    "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"

    ]

    For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.

Public Method CreateEndpointConfig(CreateEndpointConfigRequest)

Creates an endpoint configuration that 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 SageMaker to provision. Then you call the CreateEndpoint API.

Use this API if you want to use 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 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. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

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.

Public Method CreateEndpointConfigAsync(CreateEndpointConfigRequest, CancellationToken)

Creates an endpoint configuration that 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 SageMaker to provision. Then you call the CreateEndpoint API.

Use this API if you want to use 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 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. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

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.

Public Method CreateExperiment(CreateExperimentRequest)

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 SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services 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.

Public Method CreateExperimentAsync(CreateExperimentRequest, CancellationToken)

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 SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services 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.

Public Method CreateFeatureGroup(CreateFeatureGroupRequest)

Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.

The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account.

You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup.

Public Method CreateFeatureGroupAsync(CreateFeatureGroupRequest, CancellationToken)

Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.

The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account.

You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup.

Public Method CreateFlowDefinition(CreateFlowDefinitionRequest)

Creates a flow definition.

Public Method CreateFlowDefinitionAsync(CreateFlowDefinitionRequest, CancellationToken)

Creates a flow definition.

Public Method CreateHumanTaskUi(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.

Public Method CreateHumanTaskUiAsync(CreateHumanTaskUiRequest, CancellationToken)

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.

Public Method CreateHyperParameterTuningJob(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.

Public Method CreateHyperParameterTuningJobAsync(CreateHyperParameterTuningJobRequest, CancellationToken)

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.

Public Method CreateImage(CreateImageRequest)

Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Elastic Container Registry (ECR). For more information, see Bring your own SageMaker image.

Public Method CreateImageAsync(CreateImageRequest, CancellationToken)

Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Elastic Container Registry (ECR). For more information, see Bring your own SageMaker image.

Public Method CreateImageVersion(CreateImageVersionRequest)

Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon Elastic Container Registry (ECR) container image specified by BaseImage.

Public Method CreateImageVersionAsync(CreateImageVersionRequest, CancellationToken)

Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon Elastic Container Registry (ECR) container image specified by BaseImage.

Public Method CreateInferenceRecommendationsJob(CreateInferenceRecommendationsJobRequest)

Starts a recommendation job. You can create either an instance recommendation or load test job.

Public Method CreateInferenceRecommendationsJobAsync(CreateInferenceRecommendationsJobRequest, CancellationToken)

Starts a recommendation job. You can create either an instance recommendation or load test job.

Public Method CreateLabelingJob(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 Amazon Web Services 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.

You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.

Public Method CreateLabelingJobAsync(CreateLabelingJobRequest, CancellationToken)

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 Amazon Web Services 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.

You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.

Public Method CreateModel(CreateModelRequest)

Creates a model in 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 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. 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 SageMaker hosting services, see Create a Model (Amazon Web Services SDK for Python (Boto 3)).

To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.

In the request, you also provide an IAM role that 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 Amazon Web Services resources, you grant necessary permissions via this role.

Public Method CreateModelAsync(CreateModelRequest, CancellationToken)

Creates a model in 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 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. 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 SageMaker hosting services, see Create a Model (Amazon Web Services SDK for Python (Boto 3)).

To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.

In the request, you also provide an IAM role that 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 Amazon Web Services resources, you grant necessary permissions via this role.

Public Method CreateModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest)

Creates the definition for a model bias job.

Public Method CreateModelBiasJobDefinitionAsync(CreateModelBiasJobDefinitionRequest, CancellationToken)

Creates the definition for a model bias job.

Public Method CreateModelExplainabilityJobDefinition(CreateModelExplainabilityJobDefinitionRequest)

Creates the definition for a model explainability job.

Public Method CreateModelExplainabilityJobDefinitionAsync(CreateModelExplainabilityJobDefinitionRequest, CancellationToken)

Creates the definition for a model explainability job.

Public Method CreateModelPackage(CreateModelPackageRequest)

Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in 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 Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.

There are two types of model packages:

  • Versioned - a model that is part of a model group in the model registry.

  • Unversioned - a model package that is not part of a model group.

Public Method CreateModelPackageAsync(CreateModelPackageRequest, CancellationToken)

Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in 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 Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.

There are two types of model packages:

  • Versioned - a model that is part of a model group in the model registry.

  • Unversioned - a model package that is not part of a model group.

Public Method CreateModelPackageGroup(CreateModelPackageGroupRequest)

Creates a model group. A model group contains a group of model versions.

Public Method CreateModelPackageGroupAsync(CreateModelPackageGroupRequest, CancellationToken)

Creates a model group. A model group contains a group of model versions.

Public Method CreateModelQualityJobDefinition(CreateModelQualityJobDefinitionRequest)

Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

Public Method CreateModelQualityJobDefinitionAsync(CreateModelQualityJobDefinitionRequest, CancellationToken)

Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

Public Method CreateMonitoringSchedule(CreateMonitoringScheduleRequest)

Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.

Public Method CreateMonitoringScheduleAsync(CreateMonitoringScheduleRequest, CancellationToken)

Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.

Public Method CreateNotebookInstance(CreateNotebookInstanceRequest)

Creates an 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. 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.

SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.

After receiving the request, SageMaker does the following:

  1. Creates a network interface in the SageMaker VPC.

  2. (Option) If you specified SubnetId, 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, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.

  3. Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified SubnetId of your VPC, 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, SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.

After 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 SageMaker endpoints, and validate hosted models.

For more information, see How It Works.

Public Method CreateNotebookInstanceAsync(CreateNotebookInstanceRequest, CancellationToken)

Creates an 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. 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.

SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.

After receiving the request, SageMaker does the following:

  1. Creates a network interface in the SageMaker VPC.

  2. (Option) If you specified SubnetId, 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, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.

  3. Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified SubnetId of your VPC, 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, SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.

After 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 SageMaker endpoints, and validate hosted models.

For more information, see How It Works.

Public Method CreateNotebookInstanceLifecycleConfig(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.

Public Method CreateNotebookInstanceLifecycleConfigAsync(CreateNotebookInstanceLifecycleConfigRequest, CancellationToken)

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.

Public Method CreatePipeline(CreatePipelineRequest)

Creates a pipeline using a JSON pipeline definition.

Public Method CreatePipelineAsync(CreatePipelineRequest, CancellationToken)

Creates a pipeline using a JSON pipeline definition.

Public Method CreatePresignedDomainUrl(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.

The IAM role or user used to call this API defines the permissions to access the app. 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 app.

You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to SageMaker Studio Through an Interface VPC Endpoint .

The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.

Public Method CreatePresignedDomainUrlAsync(CreatePresignedDomainUrlRequest, CancellationToken)

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.

The IAM role or user used to call this API defines the permissions to access the app. 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 app.

You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to SageMaker Studio Through an Interface VPC Endpoint .

The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.

Public Method CreatePresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest)

Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker console, when you choose Open next to a notebook instance, 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 Amazon Web Services console sign-in page.

Public Method CreatePresignedNotebookInstanceUrlAsync(CreatePresignedNotebookInstanceUrlRequest, CancellationToken)

Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker console, when you choose Open next to a notebook instance, 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 Amazon Web Services console sign-in page.

Public Method CreateProcessingJob(CreateProcessingJobRequest)

Creates a processing job.

Public Method CreateProcessingJobAsync(CreateProcessingJobRequest, CancellationToken)

Creates a processing job.

Public Method CreateProject(CreateProjectRequest)

Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.

Public Method CreateProjectAsync(CreateProjectRequest, CancellationToken)

Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.

Public Method CreateStudioLifecycleConfig(CreateStudioLifecycleConfigRequest)

Creates a new Studio Lifecycle Configuration.

Public Method CreateStudioLifecycleConfigAsync(CreateStudioLifecycleConfigRequest, CancellationToken)

Creates a new Studio Lifecycle Configuration.

Public Method CreateTrainingJob(CreateTrainingJobRequest)

Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using 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 SageMaker, provided that you know how to use them for inference.

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 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 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 Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that 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 a managed spot training job has to complete.

  • Environment - The environment variables to set in the Docker container.

  • RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.

For more information about SageMaker, see How It Works.

Public Method CreateTrainingJobAsync(CreateTrainingJobRequest, CancellationToken)

Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using 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 SageMaker, provided that you know how to use them for inference.

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 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 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 Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that 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 a managed spot training job has to complete.

  • Environment - The environment variables to set in the Docker container.

  • RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.

For more information about SageMaker, see How It Works.

Public Method CreateTransformJob(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 Amazon Web Services Region in an Amazon Web Services account.

  • ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services 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.

Public Method CreateTransformJobAsync(CreateTransformJobRequest, CancellationToken)

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 Amazon Web Services Region in an Amazon Web Services account.

  • ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services 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.

Public Method CreateTrial(CreateTrialRequest)

Creates an 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 SageMaker experiment.

When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services 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.

Public Method CreateTrialAsync(CreateTrialRequest, CancellationToken)

Creates an 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 SageMaker experiment.

When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services 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.

Public Method CreateTrialComponent(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 SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services 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.

Public Method CreateTrialComponentAsync(CreateTrialComponentRequest, CancellationToken)

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 SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services 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.

Public Method CreateUserProfile(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.

Public Method CreateUserProfileAsync(CreateUserProfileRequest, CancellationToken)

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.

Public Method CreateWorkforce(CreateWorkforceRequest)

Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.

If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.

To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito).

To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).

Public Method CreateWorkforceAsync(CreateWorkforceRequest, CancellationToken)

Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.

If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.

To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito).

To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).

Public Method CreateWorkteam(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.

Public Method CreateWorkteamAsync(CreateWorkteamRequest, CancellationToken)

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.

Public Method DeleteAction(DeleteActionRequest)

Deletes an action.

Public Method DeleteActionAsync(DeleteActionRequest, CancellationToken)

Deletes an action.

Public Method DeleteAlgorithm(DeleteAlgorithmRequest)

Removes the specified algorithm from your account.

Public Method DeleteAlgorithmAsync(DeleteAlgorithmRequest, CancellationToken)

Removes the specified algorithm from your account.

Public Method DeleteApp(DeleteAppRequest)

Used to stop and delete an app.

Public Method DeleteAppAsync(DeleteAppRequest, CancellationToken)

Used to stop and delete an app.

Public Method DeleteAppImageConfig(DeleteAppImageConfigRequest)

Deletes an AppImageConfig.

Public Method DeleteAppImageConfigAsync(DeleteAppImageConfigRequest, CancellationToken)

Deletes an AppImageConfig.

Public Method DeleteArtifact(DeleteArtifactRequest)

Deletes an artifact. Either ArtifactArn or Source must be specified.

Public Method DeleteArtifactAsync(DeleteArtifactRequest, CancellationToken)

Deletes an artifact. Either ArtifactArn or Source must be specified.

Public Method DeleteAssociation(DeleteAssociationRequest)

Deletes an association.

Public Method DeleteAssociationAsync(DeleteAssociationRequest, CancellationToken)

Deletes an association.

Public Method DeleteCodeRepository(DeleteCodeRepositoryRequest)

Deletes the specified Git repository from your account.

Public Method DeleteCodeRepositoryAsync(DeleteCodeRepositoryRequest, CancellationToken)

Deletes the specified Git repository from your account.

Public Method DeleteContext(DeleteContextRequest)

Deletes an context.

Public Method DeleteContextAsync(DeleteContextRequest, CancellationToken)

Deletes an context.

Public Method DeleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest)

Deletes a data quality monitoring job definition.

Public Method DeleteDataQualityJobDefinitionAsync(DeleteDataQualityJobDefinitionRequest, CancellationToken)

Deletes a data quality monitoring job definition.

Public Method DeleteDeviceFleet(DeleteDeviceFleetRequest)

Deletes a fleet.

Public Method DeleteDeviceFleetAsync(DeleteDeviceFleetRequest, CancellationToken)

Deletes a fleet.

Public Method DeleteDomain(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.

Public Method DeleteDomainAsync(DeleteDomainRequest, CancellationToken)

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.

Public Method DeleteEndpoint(DeleteEndpointRequest)

Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.

SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.

When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do not delete or revoke the permissions for your

ExecutionRoleArn
            
, otherwise SageMaker cannot delete these resources.

Public Method DeleteEndpointAsync(DeleteEndpointRequest, CancellationToken)

Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.

SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.

When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do not delete or revoke the permissions for your

ExecutionRoleArn
            
, otherwise SageMaker cannot delete these resources.

Public Method DeleteEndpointConfig(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.

Public Method DeleteEndpointConfigAsync(DeleteEndpointConfigRequest, CancellationToken)

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.

Public Method DeleteExperiment(DeleteExperimentRequest)

Deletes an 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.

Public Method DeleteExperimentAsync(DeleteExperimentRequest, CancellationToken)

Deletes an 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.

Public Method DeleteFeatureGroup(DeleteFeatureGroupRequest)

Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.

Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted.

Public Method DeleteFeatureGroupAsync(DeleteFeatureGroupRequest, CancellationToken)

Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.

Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted.

Public Method DeleteFlowDefinition(DeleteFlowDefinitionRequest)

Deletes the specified flow definition.

Public Method DeleteFlowDefinitionAsync(DeleteFlowDefinitionRequest, CancellationToken)

Deletes the specified flow definition.

Public Method DeleteHumanTaskUi(DeleteHumanTaskUiRequest)

Use this operation to delete a human task user interface (worker task template).

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.

Public Method DeleteHumanTaskUiAsync(DeleteHumanTaskUiRequest, CancellationToken)

Use this operation to delete a human task user interface (worker task template).

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.

Public Method DeleteImage(DeleteImageRequest)

Deletes a SageMaker image and all versions of the image. The container images aren't deleted.

Public Method DeleteImageAsync(DeleteImageRequest, CancellationToken)

Deletes a SageMaker image and all versions of the image. The container images aren't deleted.

Public Method DeleteImageVersion(DeleteImageVersionRequest)

Deletes a version of a SageMaker image. The container image the version represents isn't deleted.

Public Method DeleteImageVersionAsync(DeleteImageVersionRequest, CancellationToken)

Deletes a version of a SageMaker image. The container image the version represents isn't deleted.

Public Method DeleteModel(DeleteModelRequest)

Deletes a model. The DeleteModel API deletes only the model entry that was created in 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.

Public Method DeleteModelAsync(DeleteModelRequest, CancellationToken)

Deletes a model. The DeleteModel API deletes only the model entry that was created in 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.

Public Method DeleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest)

Deletes an Amazon SageMaker model bias job definition.

Public Method DeleteModelBiasJobDefinitionAsync(DeleteModelBiasJobDefinitionRequest, CancellationToken)

Deletes an Amazon SageMaker model bias job definition.

Public Method DeleteModelExplainabilityJobDefinition(DeleteModelExplainabilityJobDefinitionRequest)

Deletes an Amazon SageMaker model explainability job definition.

Public Method DeleteModelExplainabilityJobDefinitionAsync(DeleteModelExplainabilityJobDefinitionRequest, CancellationToken)

Deletes an Amazon SageMaker model explainability job definition.

Public Method DeleteModelPackage(DeleteModelPackageRequest)

Deletes a model package.

A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

Public Method DeleteModelPackageAsync(DeleteModelPackageRequest, CancellationToken)

Deletes a model package.

A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

Public Method DeleteModelPackageGroup(DeleteModelPackageGroupRequest)

Deletes the specified model group.

Public Method DeleteModelPackageGroupAsync(DeleteModelPackageGroupRequest, CancellationToken)

Deletes the specified model group.

Public Method DeleteModelPackageGroupPolicy(DeleteModelPackageGroupPolicyRequest)

Deletes a model group resource policy.

Public Method DeleteModelPackageGroupPolicyAsync(DeleteModelPackageGroupPolicyRequest, CancellationToken)

Deletes a model group resource policy.

Public Method DeleteModelQualityJobDefinition(DeleteModelQualityJobDefinitionRequest)

Deletes the secified model quality monitoring job definition.

Public Method DeleteModelQualityJobDefinitionAsync(DeleteModelQualityJobDefinitionRequest, CancellationToken)

Deletes the secified model quality monitoring job definition.

Public Method DeleteMonitoringSchedule(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.

Public Method DeleteMonitoringScheduleAsync(DeleteMonitoringScheduleRequest, CancellationToken)

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.

Public Method DeleteNotebookInstance(DeleteNotebookInstanceRequest)

Deletes an 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. SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.

Public Method DeleteNotebookInstanceAsync(DeleteNotebookInstanceRequest, CancellationToken)

Deletes an 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. SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.

Public Method DeleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest)

Deletes a notebook instance lifecycle configuration.

Public Method DeleteNotebookInstanceLifecycleConfigAsync(DeleteNotebookInstanceLifecycleConfigRequest, CancellationToken)

Deletes a notebook instance lifecycle configuration.

Public Method DeletePipeline(DeletePipelineRequest)

Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.

Public Method DeletePipelineAsync(DeletePipelineRequest, CancellationToken)

Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.

Public Method DeleteProject(DeleteProjectRequest)

Delete the specified project.

Public Method DeleteProjectAsync(DeleteProjectRequest, CancellationToken)

Delete the specified project.

Public Method DeleteStudioLifecycleConfig(DeleteStudioLifecycleConfigRequest)

Deletes the Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.

Public Method DeleteStudioLifecycleConfigAsync(DeleteStudioLifecycleConfigRequest, CancellationToken)

Deletes the Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.

Public Method DeleteTags(DeleteTagsRequest)

Deletes the specified tags from an 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.

When you call this API to delete tags from a SageMaker Studio Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Studio Domain or User Profile launched before you called this API.

Public Method DeleteTagsAsync(DeleteTagsRequest, CancellationToken)

Deletes the specified tags from an 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.

When you call this API to delete tags from a SageMaker Studio Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Studio Domain or User Profile launched before you called this API.

Public Method DeleteTrial(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.

Public Method DeleteTrialAsync(DeleteTrialRequest, CancellationToken)

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.

Public Method DeleteTrialComponent(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.

Public Method DeleteTrialComponentAsync(DeleteTrialComponentRequest, CancellationToken)

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.

Public Method DeleteUserProfile(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.

Public Method DeleteUserProfileAsync(DeleteUserProfileRequest, CancellationToken)

Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.

Public Method DeleteWorkforce(DeleteWorkforceRequest)

Use this operation to delete a workforce.

If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.

If a private workforce contains one or more work teams, you must use the operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will recieve a ResourceInUse error.

Public Method DeleteWorkforceAsync(DeleteWorkforceRequest, CancellationToken)

Use this operation to delete a workforce.

If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.

If a private workforce contains one or more work teams, you must use the operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will recieve a ResourceInUse error.

Public Method DeleteWorkteam(DeleteWorkteamRequest)

Deletes an existing work team. This operation can't be undone.

Public Method DeleteWorkteamAsync(DeleteWorkteamRequest, CancellationToken)

Deletes an existing work team. This operation can't be undone.

Public Method DeregisterDevices(DeregisterDevicesRequest)

Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.

Public Method DeregisterDevicesAsync(DeregisterDevicesRequest, CancellationToken)

Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.

Public Method DescribeAction(DescribeActionRequest)

Describes an action.

Public Method DescribeActionAsync(DescribeActionRequest, CancellationToken)

Describes an action.

Public Method DescribeAlgorithm(DescribeAlgorithmRequest)

Returns a description of the specified algorithm that is in your account.

Public Method DescribeAlgorithmAsync(DescribeAlgorithmRequest, CancellationToken)

Returns a description of the specified algorithm that is in your account.

Public Method DescribeApp(DescribeAppRequest)

Describes the app.

Public Method DescribeAppAsync(DescribeAppRequest, CancellationToken)

Describes the app.

Public Method DescribeAppImageConfig(DescribeAppImageConfigRequest)

Describes an AppImageConfig.

Public Method DescribeAppImageConfigAsync(DescribeAppImageConfigRequest, CancellationToken)

Describes an AppImageConfig.

Public Method DescribeArtifact(DescribeArtifactRequest)

Describes an artifact.

Public Method DescribeArtifactAsync(DescribeArtifactRequest, CancellationToken)

Describes an artifact.

Public Method DescribeAutoMLJob(DescribeAutoMLJobRequest)

Returns information about an Amazon SageMaker AutoML job.

Public Method DescribeAutoMLJobAsync(DescribeAutoMLJobRequest, CancellationToken)

Returns information about an Amazon SageMaker AutoML job.

Public Method DescribeCodeRepository(DescribeCodeRepositoryRequest)

Gets details about the specified Git repository.

Public Method DescribeCodeRepositoryAsync(DescribeCodeRepositoryRequest, CancellationToken)

Gets details about the specified Git repository.

Public Method DescribeCompilationJob(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.

Public Method DescribeCompilationJobAsync(DescribeCompilationJobRequest, CancellationToken)

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.

Public Method DescribeContext(DescribeContextRequest)

Describes a context.

Public Method DescribeContextAsync(DescribeContextRequest, CancellationToken)

Describes a context.

Public Method DescribeDataQualityJobDefinition(DescribeDataQualityJobDefinitionRequest)

Gets the details of a data quality monitoring job definition.

Public Method DescribeDataQualityJobDefinitionAsync(DescribeDataQualityJobDefinitionRequest, CancellationToken)

Gets the details of a data quality monitoring job definition.

Public Method DescribeDevice(DescribeDeviceRequest)

Describes the device.

Public Method DescribeDeviceAsync(DescribeDeviceRequest, CancellationToken)

Describes the device.

Public Method DescribeDeviceFleet(DescribeDeviceFleetRequest)

A description of the fleet the device belongs to.

Public Method DescribeDeviceFleetAsync(DescribeDeviceFleetRequest, CancellationToken)

A description of the fleet the device belongs to.

Public Method DescribeDomain(DescribeDomainRequest)

The description of the domain.

Public Method DescribeDomainAsync(DescribeDomainRequest, CancellationToken)

The description of the domain.

Public Method DescribeEdgePackagingJob(DescribeEdgePackagingJobRequest)

A description of edge packaging jobs.

Public Method DescribeEdgePackagingJobAsync(DescribeEdgePackagingJobRequest, CancellationToken)

A description of edge packaging jobs.

Public Method DescribeEndpoint(DescribeEndpointRequest)

Returns the description of an endpoint.

Public Method DescribeEndpointAsync(DescribeEndpointRequest, CancellationToken)

Returns the description of an endpoint.

Public Method DescribeEndpointConfig(DescribeEndpointConfigRequest)

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

Public Method DescribeEndpointConfigAsync(DescribeEndpointConfigRequest, CancellationToken)

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

Public Method DescribeExperiment(DescribeExperimentRequest)

Provides a list of an experiment's properties.

Public Method DescribeExperimentAsync(DescribeExperimentRequest, CancellationToken)

Provides a list of an experiment's properties.

Public Method DescribeFeatureGroup(DescribeFeatureGroupRequest)

Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.

Public Method DescribeFeatureGroupAsync(DescribeFeatureGroupRequest, CancellationToken)

Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.

Public Method DescribeFeatureMetadata(DescribeFeatureMetadataRequest)

Shows the metadata for a feature within a feature group.

Public Method DescribeFeatureMetadataAsync(DescribeFeatureMetadataRequest, CancellationToken)

Shows the metadata for a feature within a feature group.

Public Method DescribeFlowDefinition(DescribeFlowDefinitionRequest)

Returns information about the specified flow definition.

Public Method DescribeFlowDefinitionAsync(DescribeFlowDefinitionRequest, CancellationToken)

Returns information about the specified flow definition.

Public Method DescribeHumanTaskUi(DescribeHumanTaskUiRequest)

Returns information about the requested human task user interface (worker task template).

Public Method DescribeHumanTaskUiAsync(DescribeHumanTaskUiRequest, CancellationToken)

Returns information about the requested human task user interface (worker task template).

Public Method DescribeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest)

Gets a description of a hyperparameter tuning job.

Public Method DescribeHyperParameterTuningJobAsync(DescribeHyperParameterTuningJobRequest, CancellationToken)

Gets a description of a hyperparameter tuning job.

Public Method DescribeImage(DescribeImageRequest)

Describes a SageMaker image.

Public Method DescribeImageAsync(DescribeImageRequest, CancellationToken)

Describes a SageMaker image.

Public Method DescribeImageVersion(DescribeImageVersionRequest)

Describes a version of a SageMaker image.

Public Method DescribeImageVersionAsync(DescribeImageVersionRequest, CancellationToken)

Describes a version of a SageMaker image.

Public Method DescribeInferenceRecommendationsJob(DescribeInferenceRecommendationsJobRequest)

Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.

Public Method DescribeInferenceRecommendationsJobAsync(DescribeInferenceRecommendationsJobRequest, CancellationToken)

Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.

Public Method DescribeLabelingJob(DescribeLabelingJobRequest)

Gets information about a labeling job.

Public Method DescribeLabelingJobAsync(DescribeLabelingJobRequest, CancellationToken)

Gets information about a labeling job.

Public Method DescribeLineageGroup(DescribeLineageGroupRequest)

Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.

Public Method DescribeLineageGroupAsync(DescribeLineageGroupRequest, CancellationToken)

Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.

Public Method DescribeModel(DescribeModelRequest)

Describes a model that you created using the CreateModel API.

Public Method DescribeModelAsync(DescribeModelRequest, CancellationToken)

Describes a model that you created using the CreateModel API.

Public Method DescribeModelBiasJobDefinition(DescribeModelBiasJobDefinitionRequest)

Returns a description of a model bias job definition.

Public Method DescribeModelBiasJobDefinitionAsync(DescribeModelBiasJobDefinitionRequest, CancellationToken)

Returns a description of a model bias job definition.

Public Method DescribeModelExplainabilityJobDefinition(DescribeModelExplainabilityJobDefinitionRequest)

Returns a description of a model explainability job definition.

Public Method DescribeModelExplainabilityJobDefinitionAsync(DescribeModelExplainabilityJobDefinitionRequest, CancellationToken)

Returns a description of a model explainability job definition.

Public Method DescribeModelPackage(DescribeModelPackageRequest)

Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.

To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.

Public Method DescribeModelPackageAsync(DescribeModelPackageRequest, CancellationToken)

Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.

To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.

Public Method DescribeModelPackageGroup(DescribeModelPackageGroupRequest)

Gets a description for the specified model group.

Public Method DescribeModelPackageGroupAsync(DescribeModelPackageGroupRequest, CancellationToken)

Gets a description for the specified model group.

Public Method DescribeModelQualityJobDefinition(DescribeModelQualityJobDefinitionRequest)

Returns a description of a model quality job definition.

Public Method DescribeModelQualityJobDefinitionAsync(DescribeModelQualityJobDefinitionRequest, CancellationToken)

Returns a description of a model quality job definition.

Public Method DescribeMonitoringSchedule(DescribeMonitoringScheduleRequest)

Describes the schedule for a monitoring job.

Public Method DescribeMonitoringScheduleAsync(DescribeMonitoringScheduleRequest, CancellationToken)

Describes the schedule for a monitoring job.

Public Method DescribeNotebookInstance(DescribeNotebookInstanceRequest)

Returns information about a notebook instance.

Public Method DescribeNotebookInstanceAsync(DescribeNotebookInstanceRequest, CancellationToken)

Returns information about a notebook instance.

Public Method DescribeNotebookInstanceLifecycleConfig(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.

Public Method DescribeNotebookInstanceLifecycleConfigAsync(DescribeNotebookInstanceLifecycleConfigRequest, CancellationToken)

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.

Public Method DescribePipeline(DescribePipelineRequest)

Describes the details of a pipeline.

Public Method DescribePipelineAsync(DescribePipelineRequest, CancellationToken)

Describes the details of a pipeline.

Public Method DescribePipelineDefinitionForExecution(DescribePipelineDefinitionForExecutionRequest)

Describes the details of an execution's pipeline definition.

Public Method DescribePipelineDefinitionForExecutionAsync(DescribePipelineDefinitionForExecutionRequest, CancellationToken)

Describes the details of an execution's pipeline definition.

Public Method DescribePipelineExecution(DescribePipelineExecutionRequest)

Describes the details of a pipeline execution.

Public Method DescribePipelineExecutionAsync(DescribePipelineExecutionRequest, CancellationToken)

Describes the details of a pipeline execution.

Public Method DescribeProcessingJob(DescribeProcessingJobRequest)

Returns a description of a processing job.

Public Method DescribeProcessingJobAsync(DescribeProcessingJobRequest, CancellationToken)

Returns a description of a processing job.

Public Method DescribeProject(DescribeProjectRequest)

Describes the details of a project.

Public Method DescribeProjectAsync(DescribeProjectRequest, CancellationToken)

Describes the details of a project.

Public Method DescribeStudioLifecycleConfig(DescribeStudioLifecycleConfigRequest)

Describes the Studio Lifecycle Configuration.

Public Method DescribeStudioLifecycleConfigAsync(DescribeStudioLifecycleConfigRequest, CancellationToken)

Describes the Studio Lifecycle Configuration.

Public Method DescribeSubscribedWorkteam(DescribeSubscribedWorkteamRequest)

Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.

Public Method DescribeSubscribedWorkteamAsync(DescribeSubscribedWorkteamRequest, CancellationToken)

Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.

Public Method DescribeTrainingJob(DescribeTrainingJobRequest)

Returns information about a training job.

Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.

Public Method DescribeTrainingJobAsync(DescribeTrainingJobRequest, CancellationToken)

Returns information about a training job.

Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.

Public Method DescribeTransformJob(DescribeTransformJobRequest)

Returns information about a transform job.

Public Method DescribeTransformJobAsync(DescribeTransformJobRequest, CancellationToken)

Returns information about a transform job.

Public Method DescribeTrial(DescribeTrialRequest)

Provides a list of a trial's properties.

Public Method DescribeTrialAsync(DescribeTrialRequest, CancellationToken)

Provides a list of a trial's properties.

Public Method DescribeTrialComponent(DescribeTrialComponentRequest)

Provides a list of a trials component's properties.

Public Method DescribeTrialComponentAsync(DescribeTrialComponentRequest, CancellationToken)

Provides a list of a trials component's properties.

Public Method DescribeUserProfile(DescribeUserProfileRequest)

Describes a user profile. For more information, see CreateUserProfile.

Public Method DescribeUserProfileAsync(DescribeUserProfileRequest, CancellationToken)

Describes a user profile. For more information, see CreateUserProfile.

Public Method DescribeWorkforce(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.

Public Method DescribeWorkforceAsync(DescribeWorkforceRequest, CancellationToken)

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.

Public Method DescribeWorkteam(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).

Public Method DescribeWorkteamAsync(DescribeWorkteamRequest, CancellationToken)

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).

Public Method DisableSagemakerServicecatalogPortfolio(DisableSagemakerServicecatalogPortfolioRequest)

Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Public Method DisableSagemakerServicecatalogPortfolioAsync(DisableSagemakerServicecatalogPortfolioRequest, CancellationToken)

Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Public Method DisassociateTrialComponent(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.

Public Method DisassociateTrialComponentAsync(DisassociateTrialComponentRequest, CancellationToken)

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.

Public Method Dispose() Inherited from Amazon.Runtime.AmazonServiceClient.
Public Method EnableSagemakerServicecatalogPortfolio(EnableSagemakerServicecatalogPortfolioRequest)

Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Public Method EnableSagemakerServicecatalogPortfolioAsync(EnableSagemakerServicecatalogPortfolioRequest, CancellationToken)

Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Public Method GetDeviceFleetReport(GetDeviceFleetReportRequest)

Describes a fleet.

Public Method GetDeviceFleetReportAsync(GetDeviceFleetReportRequest, CancellationToken)

Describes a fleet.

Public Method GetLineageGroupPolicy(GetLineageGroupPolicyRequest)

The resource policy for the lineage group.

Public Method GetLineageGroupPolicyAsync(GetLineageGroupPolicyRequest, CancellationToken)

The resource policy for the lineage group.

Public Method GetModelPackageGroupPolicy(GetModelPackageGroupPolicyRequest)

Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

Public Method GetModelPackageGroupPolicyAsync(GetModelPackageGroupPolicyRequest, CancellationToken)

Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

Public Method GetSagemakerServicecatalogPortfolioStatus(GetSagemakerServicecatalogPortfolioStatusRequest)

Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Public Method GetSagemakerServicecatalogPortfolioStatusAsync(GetSagemakerServicecatalogPortfolioStatusRequest, CancellationToken)

Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Public Method GetSearchSuggestions(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.

Public Method GetSearchSuggestionsAsync(GetSearchSuggestionsRequest, CancellationToken)

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.

Public Method ListActions(ListActionsRequest)

Lists the actions in your account and their properties.

Public Method ListActionsAsync(ListActionsRequest, CancellationToken)

Lists the actions in your account and their properties.

Public Method ListAlgorithms(ListAlgorithmsRequest)

Lists the machine learning algorithms that have been created.

Public Method ListAlgorithmsAsync(ListAlgorithmsRequest, CancellationToken)

Lists the machine learning algorithms that have been created.

Public Method ListAppImageConfigs(ListAppImageConfigsRequest)

Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.

Public Method ListAppImageConfigsAsync(ListAppImageConfigsRequest, CancellationToken)

Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.

Public Method ListApps(ListAppsRequest)

Lists apps.

Public Method ListAppsAsync(ListAppsRequest, CancellationToken)

Lists apps.

Public Method ListArtifacts(ListArtifactsRequest)

Lists the artifacts in your account and their properties.

Public Method ListArtifactsAsync(ListArtifactsRequest, CancellationToken)

Lists the artifacts in your account and their properties.

Public Method ListAssociations(ListAssociationsRequest)

Lists the associations in your account and their properties.

Public Method ListAssociationsAsync(ListAssociationsRequest, CancellationToken)

Lists the associations in your account and their properties.

Public Method ListAutoMLJobs(ListAutoMLJobsRequest)

Request a list of jobs.

Public Method ListAutoMLJobsAsync(ListAutoMLJobsRequest, CancellationToken)

Request a list of jobs.

Public Method ListCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest)

List the candidates created for the job.

Public Method ListCandidatesForAutoMLJobAsync(ListCandidatesForAutoMLJobRequest, CancellationToken)

List the candidates created for the job.

Public Method ListCodeRepositories(ListCodeRepositoriesRequest)

Gets a list of the Git repositories in your account.

Public Method ListCodeRepositoriesAsync(ListCodeRepositoriesRequest, CancellationToken)

Gets a list of the Git repositories in your account.

Public Method ListCompilationJobs(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.

Public Method ListCompilationJobsAsync(ListCompilationJobsRequest, CancellationToken)

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.

Public Method ListContexts(ListContextsRequest)

Lists the contexts in your account and their properties.

Public Method ListContextsAsync(ListContextsRequest, CancellationToken)

Lists the contexts in your account and their properties.

Public Method ListDataQualityJobDefinitions(ListDataQualityJobDefinitionsRequest)

Lists the data quality job definitions in your account.

Public Method ListDataQualityJobDefinitionsAsync(ListDataQualityJobDefinitionsRequest, CancellationToken)

Lists the data quality job definitions in your account.

Public Method ListDeviceFleets(ListDeviceFleetsRequest)

Returns a list of devices in the fleet.

Public Method ListDeviceFleetsAsync(ListDeviceFleetsRequest, CancellationToken)

Returns a list of devices in the fleet.

Public Method ListDevices(ListDevicesRequest)

A list of devices.

Public Method ListDevicesAsync(ListDevicesRequest, CancellationToken)

A list of devices.

Public Method ListDomains(ListDomainsRequest)

Lists the domains.

Public Method ListDomainsAsync(ListDomainsRequest, CancellationToken)

Lists the domains.

Public Method ListEdgePackagingJobs(ListEdgePackagingJobsRequest)

Returns a list of edge packaging jobs.

Public Method ListEdgePackagingJobsAsync(ListEdgePackagingJobsRequest, CancellationToken)

Returns a list of edge packaging jobs.

Public Method ListEndpointConfigs(ListEndpointConfigsRequest)

Lists endpoint configurations.

Public Method ListEndpointConfigsAsync(ListEndpointConfigsRequest, CancellationToken)

Lists endpoint configurations.

Public Method ListEndpoints(ListEndpointsRequest)

Lists endpoints.

Public Method ListEndpointsAsync(ListEndpointsRequest, CancellationToken)

Lists endpoints.

Public Method ListExperiments(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.

Public Method ListExperimentsAsync(ListExperimentsRequest, CancellationToken)

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.

Public Method ListFeatureGroups(ListFeatureGroupsRequest)

List FeatureGroups based on given filter and order.

Public Method ListFeatureGroupsAsync(ListFeatureGroupsRequest, CancellationToken)

List FeatureGroups based on given filter and order.

Public Method ListFlowDefinitions(ListFlowDefinitionsRequest)

Returns information about the flow definitions in your account.

Public Method ListFlowDefinitionsAsync(ListFlowDefinitionsRequest, CancellationToken)

Returns information about the flow definitions in your account.

Public Method ListHumanTaskUis(ListHumanTaskUisRequest)

Returns information about the human task user interfaces in your account.

Public Method ListHumanTaskUisAsync(ListHumanTaskUisRequest, CancellationToken)

Returns information about the human task user interfaces in your account.

Public Method ListHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest)

Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.

Public Method ListHyperParameterTuningJobsAsync(ListHyperParameterTuningJobsRequest, CancellationToken)

Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.

Public Method ListImages(ListImagesRequest)

Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.

Public Method ListImagesAsync(ListImagesRequest, CancellationToken)

Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.

Public Method ListImageVersions(ListImageVersionsRequest)

Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.

Public Method ListImageVersionsAsync(ListImageVersionsRequest, CancellationToken)

Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.

Public Method ListInferenceRecommendationsJobs(ListInferenceRecommendationsJobsRequest)

Lists recommendation jobs that satisfy various filters.

Public Method ListInferenceRecommendationsJobsAsync(ListInferenceRecommendationsJobsRequest, CancellationToken)

Lists recommendation jobs that satisfy various filters.

Public Method ListLabelingJobs(ListLabelingJobsRequest)

Gets a list of labeling jobs.

Public Method ListLabelingJobsAsync(ListLabelingJobsRequest, CancellationToken)

Gets a list of labeling jobs.

Public Method ListLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest)

Gets a list of labeling jobs assigned to a specified work team.

Public Method ListLabelingJobsForWorkteamAsync(ListLabelingJobsForWorkteamRequest, CancellationToken)

Gets a list of labeling jobs assigned to a specified work team.

Public Method ListLineageGroups(ListLineageGroupsRequest)

A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.

Public Method ListLineageGroupsAsync(ListLineageGroupsRequest, CancellationToken)

A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.

Public Method ListModelBiasJobDefinitions(ListModelBiasJobDefinitionsRequest)

Lists model bias jobs definitions that satisfy various filters.

Public Method ListModelBiasJobDefinitionsAsync(ListModelBiasJobDefinitionsRequest, CancellationToken)

Lists model bias jobs definitions that satisfy various filters.

Public Method ListModelExplainabilityJobDefinitions(ListModelExplainabilityJobDefinitionsRequest)

Lists model explainability job definitions that satisfy various filters.

Public Method ListModelExplainabilityJobDefinitionsAsync(ListModelExplainabilityJobDefinitionsRequest, CancellationToken)

Lists model explainability job definitions that satisfy various filters.

Public Method ListModelMetadata(ListModelMetadataRequest)

Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.

Public Method ListModelMetadataAsync(ListModelMetadataRequest, CancellationToken)

Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.

Public Method ListModelPackageGroups(ListModelPackageGroupsRequest)

Gets a list of the model groups in your Amazon Web Services account.

Public Method ListModelPackageGroupsAsync(ListModelPackageGroupsRequest, CancellationToken)

Gets a list of the model groups in your Amazon Web Services account.

Public Method ListModelPackages(ListModelPackagesRequest)

Lists the model packages that have been created.

Public Method ListModelPackagesAsync(ListModelPackagesRequest, CancellationToken)

Lists the model packages that have been created.

Public Method ListModelQualityJobDefinitions(ListModelQualityJobDefinitionsRequest)

Gets a list of model quality monitoring job definitions in your account.

Public Method ListModelQualityJobDefinitionsAsync(ListModelQualityJobDefinitionsRequest, CancellationToken)

Gets a list of model quality monitoring job definitions in your account.

Public Method ListModels(ListModelsRequest)

Lists models created with the CreateModel API.

Public Method ListModelsAsync(ListModelsRequest, CancellationToken)

Lists models created with the CreateModel API.

Public Method ListMonitoringExecutions(ListMonitoringExecutionsRequest)

Returns list of all monitoring job executions.

Public Method ListMonitoringExecutionsAsync(ListMonitoringExecutionsRequest, CancellationToken)

Returns list of all monitoring job executions.

Public Method ListMonitoringSchedules(ListMonitoringSchedulesRequest)

Returns list of all monitoring schedules.

Public Method ListMonitoringSchedulesAsync(ListMonitoringSchedulesRequest, CancellationToken)

Returns list of all monitoring schedules.

Public Method ListNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest)

Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

Public Method ListNotebookInstanceLifecycleConfigsAsync(ListNotebookInstanceLifecycleConfigsRequest, CancellationToken)

Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

Public Method ListNotebookInstances(ListNotebookInstancesRequest)

Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region.

Public Method ListNotebookInstancesAsync(ListNotebookInstancesRequest, CancellationToken)

Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region.

Public Method ListPipelineExecutions(ListPipelineExecutionsRequest)

Gets a list of the pipeline executions.

Public Method ListPipelineExecutionsAsync(ListPipelineExecutionsRequest, CancellationToken)

Gets a list of the pipeline executions.

Public Method ListPipelineExecutionSteps(ListPipelineExecutionStepsRequest)

Gets a list of PipeLineExecutionStep objects.

Public Method ListPipelineExecutionStepsAsync(ListPipelineExecutionStepsRequest, CancellationToken)

Gets a list of PipeLineExecutionStep objects.

Public Method ListPipelineParametersForExecution(ListPipelineParametersForExecutionRequest)

Gets a list of parameters for a pipeline execution.

Public Method ListPipelineParametersForExecutionAsync(ListPipelineParametersForExecutionRequest, CancellationToken)

Gets a list of parameters for a pipeline execution.

Public Method ListPipelines(ListPipelinesRequest)

Gets a list of pipelines.

Public Method ListPipelinesAsync(ListPipelinesRequest, CancellationToken)

Gets a list of pipelines.

Public Method ListProcessingJobs(ListProcessingJobsRequest)

Lists processing jobs that satisfy various filters.

Public Method ListProcessingJobsAsync(ListProcessingJobsRequest, CancellationToken)

Lists processing jobs that satisfy various filters.

Public Method ListProjects(ListProjectsRequest)

Gets a list of the projects in an Amazon Web Services account.

Public Method ListProjectsAsync(ListProjectsRequest, CancellationToken)

Gets a list of the projects in an Amazon Web Services account.

Public Method ListStudioLifecycleConfigs(ListStudioLifecycleConfigsRequest)

Lists the Studio Lifecycle Configurations in your Amazon Web Services Account.

Public Method ListStudioLifecycleConfigsAsync(ListStudioLifecycleConfigsRequest, CancellationToken)

Lists the Studio Lifecycle Configurations in your Amazon Web Services Account.

Public Method ListSubscribedWorkteams(ListSubscribedWorkteamsRequest)

Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

Public Method ListSubscribedWorkteamsAsync(ListSubscribedWorkteamsRequest, CancellationToken)

Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

Public Method ListTags(ListTagsRequest)

Returns the tags for the specified SageMaker resource.

Public Method ListTagsAsync(ListTagsRequest, CancellationToken)

Returns the tags for the specified SageMaker resource.

Public Method ListTrainingJobs(ListTrainingJobsRequest)

Lists training jobs.

When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response.

For example, if ListTrainingJobs is invoked with the following parameters:

{ ... MaxResults: 100, StatusEquals: InProgress ... }

First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned.

You can quickly test the API using the following Amazon Web Services CLI code.

aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress

Public Method ListTrainingJobsAsync(ListTrainingJobsRequest, CancellationToken)

Lists training jobs.

When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response.

For example, if ListTrainingJobs is invoked with the following parameters:

{ ... MaxResults: 100, StatusEquals: InProgress ... }

First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned.

You can quickly test the API using the following Amazon Web Services CLI code.

aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress

Public Method ListTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest)

Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

Public Method ListTrainingJobsForHyperParameterTuningJobAsync(ListTrainingJobsForHyperParameterTuningJobRequest, CancellationToken)

Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

Public Method ListTransformJobs(ListTransformJobsRequest)

Lists transform jobs.

Public Method ListTransformJobsAsync(ListTransformJobsRequest, CancellationToken)

Lists transform jobs.

Public Method ListTrialComponents(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

Public Method ListTrialComponentsAsync(ListTrialComponentsRequest, CancellationToken)

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

Public Method ListTrials(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.

Public Method ListTrialsAsync(ListTrialsRequest, CancellationToken)

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.

Public Method ListUserProfiles(ListUserProfilesRequest)

Lists user profiles.

Public Method ListUserProfilesAsync(ListUserProfilesRequest, CancellationToken)

Lists user profiles.

Public Method ListWorkforces(ListWorkforcesRequest)

Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.

Public Method ListWorkforcesAsync(ListWorkforcesRequest, CancellationToken)

Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.

Public Method ListWorkteams(ListWorkteamsRequest)

Gets a list of private 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.

Public Method ListWorkteamsAsync(ListWorkteamsRequest, CancellationToken)

Gets a list of private 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.

Public Method PutModelPackageGroupPolicy(PutModelPackageGroupPolicyRequest)

Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

Public Method PutModelPackageGroupPolicyAsync(PutModelPackageGroupPolicyRequest, CancellationToken)

Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

Public Method QueryLineage(QueryLineageRequest)

Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide.

Public Method QueryLineageAsync(QueryLineageRequest, CancellationToken)

Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide.

Public Method RegisterDevices(RegisterDevicesRequest)

Register devices.

Public Method RegisterDevicesAsync(RegisterDevicesRequest, CancellationToken)

Register devices.

Public Method RenderUiTemplate(RenderUiTemplateRequest)

Renders the UI template so that you can preview the worker's experience.

Public Method RenderUiTemplateAsync(RenderUiTemplateRequest, CancellationToken)

Renders the UI template so that you can preview the worker's experience.

Public Method RetryPipelineExecution(RetryPipelineExecutionRequest)

Retry the execution of the pipeline.

Public Method RetryPipelineExecutionAsync(RetryPipelineExecutionRequest, CancellationToken)

Retry the execution of the pipeline.

Public Method Search(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.

Public Method SearchAsync(SearchRequest, CancellationToken)

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.

Public Method SendPipelineExecutionStepFailure(SendPipelineExecutionStepFailureRequest)

Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Public Method SendPipelineExecutionStepFailureAsync(SendPipelineExecutionStepFailureRequest, CancellationToken)

Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Public Method SendPipelineExecutionStepSuccess(SendPipelineExecutionStepSuccessRequest)

Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Public Method SendPipelineExecutionStepSuccessAsync(SendPipelineExecutionStepSuccessRequest, CancellationToken)

Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Public Method StartMonitoringSchedule(StartMonitoringScheduleRequest)

Starts a previously stopped monitoring schedule.

By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled.

Public Method StartMonitoringScheduleAsync(StartMonitoringScheduleRequest, CancellationToken)

Starts a previously stopped monitoring schedule.

By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled.

Public Method StartNotebookInstance(StartNotebookInstanceRequest)

Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook.

Public Method StartNotebookInstanceAsync(StartNotebookInstanceRequest, CancellationToken)

Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook.

Public Method StartPipelineExecution(StartPipelineExecutionRequest)

Starts a pipeline execution.

Public Method StartPipelineExecutionAsync(StartPipelineExecutionRequest, CancellationToken)

Starts a pipeline execution.

Public Method StopAutoMLJob(StopAutoMLJobRequest)

A method for forcing the termination of a running job.

Public Method StopAutoMLJobAsync(StopAutoMLJobRequest, CancellationToken)

A method for forcing the termination of a running job.

Public Method StopCompilationJob(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.

Public Method StopCompilationJobAsync(StopCompilationJobRequest, CancellationToken)

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.

Public Method StopEdgePackagingJob(StopEdgePackagingJobRequest)

Request to stop an edge packaging job.

Public Method StopEdgePackagingJobAsync(StopEdgePackagingJobRequest, CancellationToken)

Request to stop an edge packaging job.

Public Method StopHyperParameterTuningJob(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.

Public Method StopHyperParameterTuningJobAsync(StopHyperParameterTuningJobRequest, CancellationToken)

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.

Public Method StopInferenceRecommendationsJob(StopInferenceRecommendationsJobRequest)

Stops an Inference Recommender job.

Public Method StopInferenceRecommendationsJobAsync(StopInferenceRecommendationsJobRequest, CancellationToken)

Stops an Inference Recommender job.

Public Method StopLabelingJob(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.

Public Method StopLabelingJobAsync(StopLabelingJobRequest, CancellationToken)

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.

Public Method StopMonitoringSchedule(StopMonitoringScheduleRequest)

Stops a previously started monitoring schedule.

Public Method StopMonitoringScheduleAsync(StopMonitoringScheduleRequest, CancellationToken)

Stops a previously started monitoring schedule.

Public Method StopNotebookInstance(StopNotebookInstanceRequest)

Terminates the ML compute instance. Before terminating the instance, SageMaker disconnects the ML storage volume from it. SageMaker preserves the ML storage volume. 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.

Public Method StopNotebookInstanceAsync(StopNotebookInstanceRequest, CancellationToken)

Terminates the ML compute instance. Before terminating the instance, SageMaker disconnects the ML storage volume from it. SageMaker preserves the ML storage volume. 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.

Public Method StopPipelineExecution(StopPipelineExecutionRequest)

Stops a pipeline execution.

Callback Step

A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping".

You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure.

Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.

Lambda Step

A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.

Public Method StopPipelineExecutionAsync(StopPipelineExecutionRequest, CancellationToken)

Stops a pipeline execution.

Callback Step

A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping".

You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure.

Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.

Lambda Step

A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.

Public Method StopProcessingJob(StopProcessingJobRequest)

Stops a processing job.

Public Method StopProcessingJobAsync(StopProcessingJobRequest, CancellationToken)

Stops a processing job.

Public Method StopTrainingJob(StopTrainingJobRequest)

Stops a training job. To stop a job, 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, SageMaker changes the status of the job to Stopping. After SageMaker stops the job, it sets the status to Stopped.

Public Method StopTrainingJobAsync(StopTrainingJobRequest, CancellationToken)

Stops a training job. To stop a job, 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, SageMaker changes the status of the job to Stopping. After SageMaker stops the job, it sets the status to Stopped.

Public Method StopTransformJob(StopTransformJobRequest)

Stops a batch 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 batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.

Public Method StopTransformJobAsync(StopTransformJobRequest, CancellationToken)

Stops a batch 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 batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.

Public Method UpdateAction(UpdateActionRequest)

Updates an action.

Public Method UpdateActionAsync(UpdateActionRequest, CancellationToken)

Updates an action.

Public Method UpdateAppImageConfig(UpdateAppImageConfigRequest)

Updates the properties of an AppImageConfig.

Public Method UpdateAppImageConfigAsync(UpdateAppImageConfigRequest, CancellationToken)

Updates the properties of an AppImageConfig.

Public Method UpdateArtifact(UpdateArtifactRequest)

Updates an artifact.

Public Method UpdateArtifactAsync(UpdateArtifactRequest, CancellationToken)

Updates an artifact.

Public Method UpdateCodeRepository(UpdateCodeRepositoryRequest)

Updates the specified Git repository with the specified values.

Public Method UpdateCodeRepositoryAsync(UpdateCodeRepositoryRequest, CancellationToken)

Updates the specified Git repository with the specified values.

Public Method UpdateContext(UpdateContextRequest)

Updates a context.

Public Method UpdateContextAsync(UpdateContextRequest, CancellationToken)

Updates a context.

Public Method UpdateDeviceFleet(UpdateDeviceFleetRequest)

Updates a fleet of devices.

Public Method UpdateDeviceFleetAsync(UpdateDeviceFleetRequest, CancellationToken)

Updates a fleet of devices.

Public Method UpdateDevices(UpdateDevicesRequest)

Updates one or more devices in a fleet.

Public Method UpdateDevicesAsync(UpdateDevicesRequest, CancellationToken)

Updates one or more devices in a fleet.

Public Method UpdateDomain(UpdateDomainRequest)

Updates the default settings for new user profiles in the domain.

Public Method UpdateDomainAsync(UpdateDomainRequest, CancellationToken)

Updates the default settings for new user profiles in the domain.

Public Method UpdateEndpoint(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 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.

Public Method UpdateEndpointAsync(UpdateEndpointRequest, CancellationToken)

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 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.

Public Method UpdateEndpointWeightsAndCapacities(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, 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.

Public Method UpdateEndpointWeightsAndCapacitiesAsync(UpdateEndpointWeightsAndCapacitiesRequest, CancellationToken)

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, 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.

Public Method UpdateExperiment(UpdateExperimentRequest)

Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.

Public Method UpdateExperimentAsync(UpdateExperimentRequest, CancellationToken)

Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.

Public Method UpdateFeatureGroup(UpdateFeatureGroupRequest)

Updates the feature group.

Public Method UpdateFeatureGroupAsync(UpdateFeatureGroupRequest, CancellationToken)

Updates the feature group.

Public Method UpdateFeatureMetadata(UpdateFeatureMetadataRequest)

Updates the description and parameters of the feature group.

Public Method UpdateFeatureMetadataAsync(UpdateFeatureMetadataRequest, CancellationToken)

Updates the description and parameters of the feature group.

Public Method UpdateImage(UpdateImageRequest)

Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.

Public Method UpdateImageAsync(UpdateImageRequest, CancellationToken)

Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.

Public Method UpdateModelPackage(UpdateModelPackageRequest)

Updates a versioned model.

Public Method UpdateModelPackageAsync(UpdateModelPackageRequest, CancellationToken)

Updates a versioned model.

Public Method UpdateMonitoringSchedule(UpdateMonitoringScheduleRequest)

Updates a previously created schedule.

Public Method UpdateMonitoringScheduleAsync(UpdateMonitoringScheduleRequest, CancellationToken)

Updates a previously created schedule.

Public Method UpdateNotebookInstance(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.

Public Method UpdateNotebookInstanceAsync(UpdateNotebookInstanceRequest, CancellationToken)

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.

Public Method UpdateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest)

Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.

Public Method UpdateNotebookInstanceLifecycleConfigAsync(UpdateNotebookInstanceLifecycleConfigRequest, CancellationToken)

Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.

Public Method UpdatePipeline(UpdatePipelineRequest)

Updates a pipeline.

Public Method UpdatePipelineAsync(UpdatePipelineRequest, CancellationToken)

Updates a pipeline.

Public Method UpdatePipelineExecution(UpdatePipelineExecutionRequest)

Updates a pipeline execution.

Public Method UpdatePipelineExecutionAsync(UpdatePipelineExecutionRequest, CancellationToken)

Updates a pipeline execution.

Public Method UpdateProject(UpdateProjectRequest)

Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.

You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.

Public Method UpdateProjectAsync(UpdateProjectRequest, CancellationToken)

Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.

You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.

Public Method UpdateTrainingJob(UpdateTrainingJobRequest)

Update a model training job to request a new Debugger profiling configuration.

Public Method UpdateTrainingJobAsync(UpdateTrainingJobRequest, CancellationToken)

Update a model training job to request a new Debugger profiling configuration.

Public Method UpdateTrial(UpdateTrialRequest)

Updates the display name of a trial.

Public Method UpdateTrialAsync(UpdateTrialRequest, CancellationToken)

Updates the display name of a trial.

Public Method UpdateTrialComponent(UpdateTrialComponentRequest)

Updates one or more properties of a trial component.

Public Method UpdateTrialComponentAsync(UpdateTrialComponentRequest, CancellationToken)

Updates one or more properties of a trial component.

Public Method UpdateUserProfile(UpdateUserProfileRequest)

Updates a user profile.

Public Method UpdateUserProfileAsync(UpdateUserProfileRequest, CancellationToken)

Updates a user profile.

Public Method UpdateWorkforce(UpdateWorkforceRequest)

Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.

The worker portal is now supported in VPC and public internet.

Use SourceIpConfig to restrict worker access to tasks to a specific range 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 and get a Not Found error message on the worker portal.

To restrict access to all the workers in public internet, add the SourceIpConfig CIDR value as "0.0.0.0/0".

Amazon SageMaker does not support Source Ip restriction for worker portals in VPC.

Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP.

You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the operation.

After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the operation.

This operation only applies to private workforces.

Public Method UpdateWorkforceAsync(UpdateWorkforceRequest, CancellationToken)

Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.

The worker portal is now supported in VPC and public internet.

Use SourceIpConfig to restrict worker access to tasks to a specific range 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 and get a Not Found error message on the worker portal.

To restrict access to all the workers in public internet, add the SourceIpConfig CIDR value as "0.0.0.0/0".

Amazon SageMaker does not support Source Ip restriction for worker portals in VPC.

Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP.

You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the operation.

After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the operation.

This operation only applies to private workforces.

Public Method UpdateWorkteam(UpdateWorkteamRequest)

Updates an existing work team with new member definitions or description.

Public Method UpdateWorkteamAsync(UpdateWorkteamRequest, CancellationToken)

Updates an existing work team with new member definitions or description.

Events

NameDescription
Event AfterResponseEvent Inherited from Amazon.Runtime.AmazonServiceClient.
Event BeforeRequestEvent Inherited from Amazon.Runtime.AmazonServiceClient.
Event ExceptionEvent Inherited from Amazon.Runtime.AmazonServiceClient.

Version Information

.NET Core App:
Supported in: 3.1

.NET Standard:
Supported in: 2.0

.NET Framework:
Supported in: 4.5, 4.0, 3.5