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Man Pages
Paws::SageMaker(3) User Contributed Perl Documentation Paws::SageMaker(3)

Paws::SageMaker - Perl Interface to AWS Amazon SageMaker Service

  use Paws;
  my $obj = Paws->service('SageMaker');
  my $res = $obj->Method(
    Arg1 => $val1,
    Arg2 => [ 'V1', 'V2' ],
    # if Arg3 is an object, the HashRef will be used as arguments to the constructor
    # of the arguments type
    Arg3 => { Att1 => 'Val1' },
    # if Arg4 is an array of objects, the HashRefs will be passed as arguments to
    # the constructor of the arguments type
    Arg4 => [ { Att1 => 'Val1'  }, { Att1 => 'Val2' } ],
  );

Provides APIs for creating and managing Amazon SageMaker resources.

Other Resources:

  • Amazon SageMaker Developer Guide (https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html#first-time-user)
  • Amazon Augmented AI Runtime API Reference (https://docs.aws.amazon.com/augmented-ai/2019-11-07/APIReference/Welcome.html)

For the AWS API documentation, see <https://docs.aws.amazon.com/goto/WebAPI/api.sagemaker-2017-07-24>

[AssociationType => Str]

Each argument is described in detail in: Paws::SageMaker::AddAssociation

Returns: a Paws::SageMaker::AddAssociationResponse instance

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html).

Each argument is described in detail in: Paws::SageMaker::AddTags

Returns: a Paws::SageMaker::AddTagsOutput instance

Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.

Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies (https://aws.amazon.com/answers/account-management/aws-tagging-strategies/).

Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the "Tags" parameter of CreateHyperParameterTuningJob

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.

Each argument is described in detail in: Paws::SageMaker::AssociateTrialComponent

Returns: a Paws::SageMaker::AssociateTrialComponentResponse instance

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.

[Description => Str]
[MetadataProperties => Paws::SageMaker::MetadataProperties]
[Properties => Paws::SageMaker::LineageEntityParameters]
[Status => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateAction

Returns: a Paws::SageMaker::CreateActionResponse instance

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html).

"CreateAction" can only be invoked from within an SageMaker managed environment. This includes SageMaker training jobs, processing jobs, transform jobs, and SageMaker notebooks. A call to "CreateAction" from outside one of these environments results in an error.

[AlgorithmDescription => Str]
[CertifyForMarketplace => Bool]
[InferenceSpecification => Paws::SageMaker::InferenceSpecification]
[Tags => ArrayRef[Paws::SageMaker::Tag]]
[ValidationSpecification => Paws::SageMaker::AlgorithmValidationSpecification]

Each argument is described in detail in: Paws::SageMaker::CreateAlgorithm

Returns: a Paws::SageMaker::CreateAlgorithmOutput instance

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

[ResourceSpec => Paws::SageMaker::ResourceSpec]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateApp

Returns: a Paws::SageMaker::CreateAppResponse instance

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.

[KernelGatewayImageConfig => Paws::SageMaker::KernelGatewayImageConfig]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateAppImageConfig

Returns: a Paws::SageMaker::CreateAppImageConfigResponse instance

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.

[ArtifactName => Str]
[MetadataProperties => Paws::SageMaker::MetadataProperties]
[Properties => Paws::SageMaker::LineageEntityParameters]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateArtifact

Returns: a Paws::SageMaker::CreateArtifactResponse instance

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html).

"CreateArtifact" can only be invoked from within an SageMaker managed environment. This includes SageMaker training jobs, processing jobs, transform jobs, and SageMaker notebooks. A call to "CreateArtifact" from outside one of these environments results in an error.

[AutoMLJobConfig => Paws::SageMaker::AutoMLJobConfig]
[AutoMLJobObjective => Paws::SageMaker::AutoMLJobObjective]
[GenerateCandidateDefinitionsOnly => Bool]
[ModelDeployConfig => Paws::SageMaker::ModelDeployConfig]
[ProblemType => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateAutoMLJob

Returns: a Paws::SageMaker::CreateAutoMLJobResponse instance

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html).

[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateCodeRepository

Returns: a Paws::SageMaker::CreateCodeRepositoryOutput instance

Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.

The repository can be hosted either in Amazon Web Services CodeCommit (https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html) or in any other Git repository.

[Tags => ArrayRef[Paws::SageMaker::Tag]]
[VpcConfig => Paws::SageMaker::NeoVpcConfig]

Each argument is described in detail in: Paws::SageMaker::CreateCompilationJob

Returns: a Paws::SageMaker::CreateCompilationJobResponse instance

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.

[Description => Str]
[Properties => Paws::SageMaker::LineageEntityParameters]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateContext

Returns: a Paws::SageMaker::CreateContextResponse instance

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html).

"CreateContext" can only be invoked from within an SageMaker managed environment. This includes SageMaker training jobs, processing jobs, transform jobs, and SageMaker notebooks. A call to "CreateContext" from outside one of these environments results in an error.

[DataQualityBaselineConfig => Paws::SageMaker::DataQualityBaselineConfig]
[NetworkConfig => Paws::SageMaker::MonitoringNetworkConfig]
[StoppingCondition => Paws::SageMaker::MonitoringStoppingCondition]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateDataQualityJobDefinition

Returns: a Paws::SageMaker::CreateDataQualityJobDefinitionResponse instance

Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html).

[Description => Str]
[EnableIotRoleAlias => Bool]
[RoleArn => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateDeviceFleet

Returns: nothing

Creates a device fleet.

[AppNetworkAccessType => Str]
[HomeEfsFileSystemKmsKeyId => Str]
[KmsKeyId => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateDomain

Returns: a Paws::SageMaker::CreateDomainResponse instance

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 customer master key (CMK) by default. For more control, you can specify a customer managed CMK. For more information, see Protect Data at Rest Using Encryption (https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html).

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/studio-notebooks-and-internet-access.html).

[ResourceKey => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateEdgePackagingJob

Returns: nothing

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.

[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateEndpoint

Returns: a Paws::SageMaker::CreateEndpointOutput instance

Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.

Use this API to deploy models using Amazon SageMaker hosting services.

For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto 3)). (https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-deploy-model.html#ex1-deploy-model-boto)

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, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.

When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting "Eventually Consistent Reads" (https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html), the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

When Amazon SageMaker receives the request, it sets the endpoint status to "Creating". After it creates the endpoint, it sets the status to "InService". Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.

If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses 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 (https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html) 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 (https://console.aws.amazon.com/iam/), 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 Amazon 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 Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference (https://docs.aws.amazon.com/sagemaker/latest/dg/api-permissions-reference.html).

[DataCaptureConfig => Paws::SageMaker::DataCaptureConfig]
[KmsKeyId => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateEndpointConfig

Returns: a Paws::SageMaker::CreateEndpointConfigOutput instance

Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the "CreateModel" API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.

Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.

In the request, you define a "ProductionVariant", for each model that you want to deploy. Each "ProductionVariant" parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.

If you are hosting multiple models, you also assign a "VariantWeight" to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto 3)). (https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-deploy-model.html#ex1-deploy-model-boto)

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" (https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html), 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.

[Description => Str]
[DisplayName => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateExperiment

Returns: a Paws::SageMaker::CreateExperimentResponse instance

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.

[Description => Str]
[OfflineStoreConfig => Paws::SageMaker::OfflineStoreConfig]
[OnlineStoreConfig => Paws::SageMaker::OnlineStoreConfig]
[RoleArn => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateFeatureGroup

Returns: a Paws::SageMaker::CreateFeatureGroupResponse instance

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 (https://docs.aws.amazon.com/general/latest/gr/aws_service_limits.html) to see the "FeatureGroup"s quota for your Amazon Web Services account.

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

[HumanLoopActivationConfig => Paws::SageMaker::HumanLoopActivationConfig]
[HumanLoopRequestSource => Paws::SageMaker::HumanLoopRequestSource]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateFlowDefinition

Returns: a Paws::SageMaker::CreateFlowDefinitionResponse instance

Creates a flow definition.

[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateHumanTaskUi

Returns: a Paws::SageMaker::CreateHumanTaskUiResponse instance

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.

[Tags => ArrayRef[Paws::SageMaker::Tag]]
[TrainingJobDefinition => Paws::SageMaker::HyperParameterTrainingJobDefinition]
[TrainingJobDefinitions => ArrayRef[Paws::SageMaker::HyperParameterTrainingJobDefinition]]
[WarmStartConfig => Paws::SageMaker::HyperParameterTuningJobWarmStartConfig]

Each argument is described in detail in: Paws::SageMaker::CreateHyperParameterTuningJob

Returns: a Paws::SageMaker::CreateHyperParameterTuningJobResponse instance

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.

[Description => Str]
[DisplayName => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateImage

Returns: a Paws::SageMaker::CreateImageResponse instance

Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image (https://docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi.html).

Each argument is described in detail in: Paws::SageMaker::CreateImageVersion

Returns: a Paws::SageMaker::CreateImageVersionResponse instance

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

[LabelCategoryConfigS3Uri => Str]
[LabelingJobAlgorithmsConfig => Paws::SageMaker::LabelingJobAlgorithmsConfig]
[StoppingConditions => Paws::SageMaker::LabelingJobStoppingConditions]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateLabelingJob

Returns: a Paws::SageMaker::CreateLabelingJobResponse instance

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-automated-labeling.html).

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-data.html).

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) (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-create-labeling-job-api.html) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-streaming-create-job.html).

[Containers => ArrayRef[Paws::SageMaker::ContainerDefinition]]
[EnableNetworkIsolation => Bool]
[InferenceExecutionConfig => Paws::SageMaker::InferenceExecutionConfig]
[PrimaryContainer => Paws::SageMaker::ContainerDefinition]
[Tags => ArrayRef[Paws::SageMaker::Tag]]
[VpcConfig => Paws::SageMaker::VpcConfig]

Each argument is described in detail in: Paws::SageMaker::CreateModel

Returns: a Paws::SageMaker::CreateModelOutput instance

Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.

Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.

To host your model, you create an endpoint configuration with the "CreateEndpointConfig" API, and then create an endpoint with the "CreateEndpoint" API. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.

For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto 3)). (https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-deploy-model.html#ex1-deploy-model-boto)

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

In the "CreateModel" request, you must define a container with the "PrimaryContainer" parameter.

In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.

[ModelBiasBaselineConfig => Paws::SageMaker::ModelBiasBaselineConfig]
[NetworkConfig => Paws::SageMaker::MonitoringNetworkConfig]
[StoppingCondition => Paws::SageMaker::MonitoringStoppingCondition]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateModelBiasJobDefinition

Returns: a Paws::SageMaker::CreateModelBiasJobDefinitionResponse instance

Creates the definition for a model bias job.

[ModelExplainabilityBaselineConfig => Paws::SageMaker::ModelExplainabilityBaselineConfig]
[NetworkConfig => Paws::SageMaker::MonitoringNetworkConfig]
[StoppingCondition => Paws::SageMaker::MonitoringStoppingCondition]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateModelExplainabilityJobDefinition

Returns: a Paws::SageMaker::CreateModelExplainabilityJobDefinitionResponse instance

Creates the definition for a model explainability job.

[CertifyForMarketplace => Bool]
[ClientToken => Str]
[InferenceSpecification => Paws::SageMaker::InferenceSpecification]
[MetadataProperties => Paws::SageMaker::MetadataProperties]
[ModelApprovalStatus => Str]
[ModelMetrics => Paws::SageMaker::ModelMetrics]
[ModelPackageDescription => Str]
[ModelPackageGroupName => Str]
[ModelPackageName => Str]
[SourceAlgorithmSpecification => Paws::SageMaker::SourceAlgorithmSpecification]
[Tags => ArrayRef[Paws::SageMaker::Tag]]
[ValidationSpecification => Paws::SageMaker::ModelPackageValidationSpecification]

Each argument is described in detail in: Paws::SageMaker::CreateModelPackage

Returns: a Paws::SageMaker::CreateModelPackageOutput instance

Creates a model package that you can use to create Amazon 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 Amazon SageMaker.

To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for "InferenceSpecification". To create a model from an algorithm resource that you created or subscribed to in 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.

[ModelPackageGroupDescription => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateModelPackageGroup

Returns: a Paws::SageMaker::CreateModelPackageGroupOutput instance

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

[ModelQualityBaselineConfig => Paws::SageMaker::ModelQualityBaselineConfig]
[NetworkConfig => Paws::SageMaker::MonitoringNetworkConfig]
[StoppingCondition => Paws::SageMaker::MonitoringStoppingCondition]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateModelQualityJobDefinition

Returns: a Paws::SageMaker::CreateModelQualityJobDefinitionResponse instance

Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html).

[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateMonitoringSchedule

Returns: a Paws::SageMaker::CreateMonitoringScheduleResponse instance

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

[AcceleratorTypes => ArrayRef[Str|Undef]]
[AdditionalCodeRepositories => ArrayRef[Str|Undef]]
[DefaultCodeRepository => Str]
[DirectInternetAccess => Str]
[KmsKeyId => Str]
[LifecycleConfigName => Str]
[RootAccess => Str]
[SecurityGroupIds => ArrayRef[Str|Undef]]
[SubnetId => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]
[VolumeSizeInGB => Int]

Each argument is described in detail in: Paws::SageMaker::CreateNotebookInstance

Returns: a Paws::SageMaker::CreateNotebookInstanceOutput instance

Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.

In a "CreateNotebookInstance" request, specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.

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

After receiving the request, Amazon SageMaker does the following:

1.
Creates a network interface in the Amazon SageMaker VPC.
2.
(Option) If you specified "SubnetId", Amazon SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.
3.
Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified "SubnetId" of your VPC, Amazon SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.

After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.

After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.

For more information, see How It Works (https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html).

[OnCreate => ArrayRef[Paws::SageMaker::NotebookInstanceLifecycleHook]]
[OnStart => ArrayRef[Paws::SageMaker::NotebookInstanceLifecycleHook]]

Each argument is described in detail in: Paws::SageMaker::CreateNotebookInstanceLifecycleConfig

Returns: a Paws::SageMaker::CreateNotebookInstanceLifecycleConfigOutput instance

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html).

[PipelineDescription => Str]
[PipelineDisplayName => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreatePipeline

Returns: a Paws::SageMaker::CreatePipelineResponse instance

Creates a pipeline using a JSON pipeline definition.

[ExpiresInSeconds => Int]
[SessionExpirationDurationInSeconds => Int]

Each argument is described in detail in: Paws::SageMaker::CreatePresignedDomainUrl

Returns: a Paws::SageMaker::CreatePresignedDomainUrlResponse instance

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/studio-interface-endpoint.html) .

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.

[SessionExpirationDurationInSeconds => Int]

Each argument is described in detail in: Paws::SageMaker::CreatePresignedNotebookInstanceUrl

Returns: a Paws::SageMaker::CreatePresignedNotebookInstanceUrlOutput instance

Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose "Open" next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.

The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.

You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the "NotIpAddress" condition operator and the "aws:SourceIP" condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address (https://docs.aws.amazon.com/sagemaker/latest/dg/security_iam_id-based-policy-examples.html#nbi-ip-filter).

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.

[Environment => Paws::SageMaker::ProcessingEnvironmentMap]
[ExperimentConfig => Paws::SageMaker::ExperimentConfig]
[NetworkConfig => Paws::SageMaker::NetworkConfig]
[ProcessingInputs => ArrayRef[Paws::SageMaker::ProcessingInput]]
[ProcessingOutputConfig => Paws::SageMaker::ProcessingOutputConfig]
[StoppingCondition => Paws::SageMaker::ProcessingStoppingCondition]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateProcessingJob

Returns: a Paws::SageMaker::CreateProcessingJobResponse instance

Creates a processing job.

[ProjectDescription => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateProject

Returns: a Paws::SageMaker::CreateProjectOutput instance

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.

[CheckpointConfig => Paws::SageMaker::CheckpointConfig]
[DebugHookConfig => Paws::SageMaker::DebugHookConfig]
[DebugRuleConfigurations => ArrayRef[Paws::SageMaker::DebugRuleConfiguration]]
[EnableInterContainerTrafficEncryption => Bool]
[EnableManagedSpotTraining => Bool]
[EnableNetworkIsolation => Bool]
[Environment => Paws::SageMaker::TrainingEnvironmentMap]
[ExperimentConfig => Paws::SageMaker::ExperimentConfig]
[HyperParameters => Paws::SageMaker::HyperParameters]
[InputDataConfig => ArrayRef[Paws::SageMaker::Channel]]
[ProfilerConfig => Paws::SageMaker::ProfilerConfig]
[ProfilerRuleConfigurations => ArrayRef[Paws::SageMaker::ProfilerRuleConfiguration]]
[RetryStrategy => Paws::SageMaker::RetryStrategy]
[Tags => ArrayRef[Paws::SageMaker::Tag]]
[TensorBoardOutputConfig => Paws::SageMaker::TensorBoardOutputConfig]
[VpcConfig => Paws::SageMaker::VpcConfig]

Each argument is described in detail in: Paws::SageMaker::CreateTrainingJob

Returns: a Paws::SageMaker::CreateTrainingJobResponse instance

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

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker, provided that you know how to use them for 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 Amazon SageMaker, see Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).
  • "InputDataConfig" - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is stored.
  • "OutputDataConfig" - Identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of model training.
  • "ResourceConfig" - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.
  • "EnableManagedSpotTraining" - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training (https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html).
  • "RoleArn" - The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training.
  • "StoppingCondition" - To help cap training costs, use "MaxRuntimeInSeconds" to set a time limit for training. Use "MaxWaitTimeInSeconds" to specify how long 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 Amazon SageMaker, see How It Works (https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html).

[BatchStrategy => Str]
[DataProcessing => Paws::SageMaker::DataProcessing]
[Environment => Paws::SageMaker::TransformEnvironmentMap]
[ExperimentConfig => Paws::SageMaker::ExperimentConfig]
[MaxConcurrentTransforms => Int]
[MaxPayloadInMB => Int]
[ModelClientConfig => Paws::SageMaker::ModelClientConfig]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateTransformJob

Returns: a Paws::SageMaker::CreateTransformJobResponse instance

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html).

[DisplayName => Str]
[MetadataProperties => Paws::SageMaker::MetadataProperties]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateTrial

Returns: a Paws::SageMaker::CreateTrialResponse instance

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.

[DisplayName => Str]
[EndTime => Str]
[InputArtifacts => Paws::SageMaker::TrialComponentArtifacts]
[MetadataProperties => Paws::SageMaker::MetadataProperties]
[OutputArtifacts => Paws::SageMaker::TrialComponentArtifacts]
[Parameters => Paws::SageMaker::TrialComponentParameters]
[StartTime => Str]
[Status => Paws::SageMaker::TrialComponentStatus]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateTrialComponent

Returns: a Paws::SageMaker::CreateTrialComponentResponse instance

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.

[SingleSignOnUserIdentifier => Str]
[SingleSignOnUserValue => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]
[UserSettings => Paws::SageMaker::UserSettings]

Each argument is described in detail in: Paws::SageMaker::CreateUserProfile

Returns: a Paws::SageMaker::CreateUserProfileResponse instance

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.

[CognitoConfig => Paws::SageMaker::CognitoConfig]
[OidcConfig => Paws::SageMaker::OidcConfig]
[SourceIpConfig => Paws::SageMaker::SourceIpConfig]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::CreateWorkforce

Returns: a Paws::SageMaker::CreateWorkforceResponse instance

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) (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-create-private.html).

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) (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-create-private-oidc.html).

[NotificationConfiguration => Paws::SageMaker::NotificationConfiguration]
[Tags => ArrayRef[Paws::SageMaker::Tag]]
[WorkforceName => Str]

Each argument is described in detail in: Paws::SageMaker::CreateWorkteam

Returns: a Paws::SageMaker::CreateWorkteamResponse instance

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.

Each argument is described in detail in: Paws::SageMaker::DeleteAction

Returns: a Paws::SageMaker::DeleteActionResponse instance

Deletes an action.

Each argument is described in detail in: Paws::SageMaker::DeleteAlgorithm

Returns: nothing

Removes the specified algorithm from your account.

Each argument is described in detail in: Paws::SageMaker::DeleteApp

Returns: nothing

Used to stop and delete an app.

Each argument is described in detail in: Paws::SageMaker::DeleteAppImageConfig

Returns: nothing

Deletes an AppImageConfig.

[ArtifactArn => Str]
[Source => Paws::SageMaker::ArtifactSource]

Each argument is described in detail in: Paws::SageMaker::DeleteArtifact

Returns: a Paws::SageMaker::DeleteArtifactResponse instance

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

Each argument is described in detail in: Paws::SageMaker::DeleteAssociation

Returns: a Paws::SageMaker::DeleteAssociationResponse instance

Deletes an association.

Each argument is described in detail in: Paws::SageMaker::DeleteCodeRepository

Returns: nothing

Deletes the specified Git repository from your account.

Each argument is described in detail in: Paws::SageMaker::DeleteContext

Returns: a Paws::SageMaker::DeleteContextResponse instance

Deletes an context.

Each argument is described in detail in: Paws::SageMaker::DeleteDataQualityJobDefinition

Returns: nothing

Deletes a data quality monitoring job definition.

Each argument is described in detail in: Paws::SageMaker::DeleteDeviceFleet

Returns: nothing

Deletes a fleet.

[RetentionPolicy => Paws::SageMaker::RetentionPolicy]

Each argument is described in detail in: Paws::SageMaker::DeleteDomain

Returns: nothing

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.

Each argument is described in detail in: Paws::SageMaker::DeleteEndpoint

Returns: nothing

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

Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant (http://docs.aws.amazon.com/kms/latest/APIReference/API_RevokeGrant.html) API call.

Each argument is described in detail in: Paws::SageMaker::DeleteEndpointConfig

Returns: nothing

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.

Each argument is described in detail in: Paws::SageMaker::DeleteExperiment

Returns: a Paws::SageMaker::DeleteExperimentResponse instance

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.

Each argument is described in detail in: Paws::SageMaker::DeleteFeatureGroup

Returns: nothing

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.

Each argument is described in detail in: Paws::SageMaker::DeleteFlowDefinition

Returns: a Paws::SageMaker::DeleteFlowDefinitionResponse instance

Deletes the specified flow definition.

Each argument is described in detail in: Paws::SageMaker::DeleteHumanTaskUi

Returns: a Paws::SageMaker::DeleteHumanTaskUiResponse instance

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".

Each argument is described in detail in: Paws::SageMaker::DeleteImage

Returns: a Paws::SageMaker::DeleteImageResponse instance

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

Each argument is described in detail in: Paws::SageMaker::DeleteImageVersion

Returns: a Paws::SageMaker::DeleteImageVersionResponse instance

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

Each argument is described in detail in: Paws::SageMaker::DeleteModel

Returns: nothing

Deletes a model. The "DeleteModel" API deletes only the model entry that was created in Amazon SageMaker when you called the "CreateModel" API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.

Each argument is described in detail in: Paws::SageMaker::DeleteModelBiasJobDefinition

Returns: nothing

Deletes an Amazon SageMaker model bias job definition.

Each argument is described in detail in: Paws::SageMaker::DeleteModelExplainabilityJobDefinition

Returns: nothing

Deletes an Amazon SageMaker model explainability job definition.

Each argument is described in detail in: Paws::SageMaker::DeleteModelPackage

Returns: nothing

Deletes a model package.

A model package is used to create Amazon 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 Amazon SageMaker.

Each argument is described in detail in: Paws::SageMaker::DeleteModelPackageGroup

Returns: nothing

Deletes the specified model group.

Each argument is described in detail in: Paws::SageMaker::DeleteModelPackageGroupPolicy

Returns: nothing

Deletes a model group resource policy.

Each argument is described in detail in: Paws::SageMaker::DeleteModelQualityJobDefinition

Returns: nothing

Deletes the secified model quality monitoring job definition.

Each argument is described in detail in: Paws::SageMaker::DeleteMonitoringSchedule

Returns: nothing

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.

Each argument is described in detail in: Paws::SageMaker::DeleteNotebookInstance

Returns: nothing

Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the "StopNotebookInstance" API.

When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.

Each argument is described in detail in: Paws::SageMaker::DeleteNotebookInstanceLifecycleConfig

Returns: nothing

Deletes a notebook instance lifecycle configuration.

Each argument is described in detail in: Paws::SageMaker::DeletePipeline

Returns: a Paws::SageMaker::DeletePipelineResponse instance

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.

Each argument is described in detail in: Paws::SageMaker::DeleteProject

Returns: nothing

Delete the specified project.

Each argument is described in detail in: Paws::SageMaker::DeleteTags

Returns: a Paws::SageMaker::DeleteTagsOutput instance

Deletes the specified tags from an Amazon SageMaker resource.

To list a resource's tags, use the "ListTags" API.

When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.

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.

Each argument is described in detail in: Paws::SageMaker::DeleteTrial

Returns: a Paws::SageMaker::DeleteTrialResponse instance

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.

Each argument is described in detail in: Paws::SageMaker::DeleteTrialComponent

Returns: a Paws::SageMaker::DeleteTrialComponentResponse instance

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.

Each argument is described in detail in: Paws::SageMaker::DeleteUserProfile

Returns: nothing

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

Each argument is described in detail in: Paws::SageMaker::DeleteWorkforce

Returns: a Paws::SageMaker::DeleteWorkforceResponse instance

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.

Each argument is described in detail in: Paws::SageMaker::DeleteWorkteam

Returns: a Paws::SageMaker::DeleteWorkteamResponse instance

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

Each argument is described in detail in: Paws::SageMaker::DeregisterDevices

Returns: nothing

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

Each argument is described in detail in: Paws::SageMaker::DescribeAction

Returns: a Paws::SageMaker::DescribeActionResponse instance

Describes an action.

Each argument is described in detail in: Paws::SageMaker::DescribeAlgorithm

Returns: a Paws::SageMaker::DescribeAlgorithmOutput instance

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

Each argument is described in detail in: Paws::SageMaker::DescribeApp

Returns: a Paws::SageMaker::DescribeAppResponse instance

Describes the app.

Each argument is described in detail in: Paws::SageMaker::DescribeAppImageConfig

Returns: a Paws::SageMaker::DescribeAppImageConfigResponse instance

Describes an AppImageConfig.

Each argument is described in detail in: Paws::SageMaker::DescribeArtifact

Returns: a Paws::SageMaker::DescribeArtifactResponse instance

Describes an artifact.

Each argument is described in detail in: Paws::SageMaker::DescribeAutoMLJob

Returns: a Paws::SageMaker::DescribeAutoMLJobResponse instance

Returns information about an Amazon SageMaker AutoML job.

Each argument is described in detail in: Paws::SageMaker::DescribeCodeRepository

Returns: a Paws::SageMaker::DescribeCodeRepositoryOutput instance

Gets details about the specified Git repository.

Each argument is described in detail in: Paws::SageMaker::DescribeCompilationJob

Returns: a Paws::SageMaker::DescribeCompilationJobResponse instance

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.

Each argument is described in detail in: Paws::SageMaker::DescribeContext

Returns: a Paws::SageMaker::DescribeContextResponse instance

Describes a context.

Each argument is described in detail in: Paws::SageMaker::DescribeDataQualityJobDefinition

Returns: a Paws::SageMaker::DescribeDataQualityJobDefinitionResponse instance

Gets the details of a data quality monitoring job definition.

[NextToken => Str]

Each argument is described in detail in: Paws::SageMaker::DescribeDevice

Returns: a Paws::SageMaker::DescribeDeviceResponse instance

Describes the device.

Each argument is described in detail in: Paws::SageMaker::DescribeDeviceFleet

Returns: a Paws::SageMaker::DescribeDeviceFleetResponse instance

A description of the fleet the device belongs to.

Each argument is described in detail in: Paws::SageMaker::DescribeDomain

Returns: a Paws::SageMaker::DescribeDomainResponse instance

The description of the domain.

Each argument is described in detail in: Paws::SageMaker::DescribeEdgePackagingJob

Returns: a Paws::SageMaker::DescribeEdgePackagingJobResponse instance

A description of edge packaging jobs.

Each argument is described in detail in: Paws::SageMaker::DescribeEndpoint

Returns: a Paws::SageMaker::DescribeEndpointOutput instance

Returns the description of an endpoint.

Each argument is described in detail in: Paws::SageMaker::DescribeEndpointConfig

Returns: a Paws::SageMaker::DescribeEndpointConfigOutput instance

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

Each argument is described in detail in: Paws::SageMaker::DescribeExperiment

Returns: a Paws::SageMaker::DescribeExperimentResponse instance

Provides a list of an experiment's properties.

[NextToken => Str]

Each argument is described in detail in: Paws::SageMaker::DescribeFeatureGroup

Returns: a Paws::SageMaker::DescribeFeatureGroupResponse instance

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.

Each argument is described in detail in: Paws::SageMaker::DescribeFlowDefinition

Returns: a Paws::SageMaker::DescribeFlowDefinitionResponse instance

Returns information about the specified flow definition.

Each argument is described in detail in: Paws::SageMaker::DescribeHumanTaskUi

Returns: a Paws::SageMaker::DescribeHumanTaskUiResponse instance

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

Each argument is described in detail in: Paws::SageMaker::DescribeHyperParameterTuningJob

Returns: a Paws::SageMaker::DescribeHyperParameterTuningJobResponse instance

Gets a description of a hyperparameter tuning job.

Each argument is described in detail in: Paws::SageMaker::DescribeImage

Returns: a Paws::SageMaker::DescribeImageResponse instance

Describes a SageMaker image.

[Version => Int]

Each argument is described in detail in: Paws::SageMaker::DescribeImageVersion

Returns: a Paws::SageMaker::DescribeImageVersionResponse instance

Describes a version of a SageMaker image.

Each argument is described in detail in: Paws::SageMaker::DescribeLabelingJob

Returns: a Paws::SageMaker::DescribeLabelingJobResponse instance

Gets information about a labeling job.

Each argument is described in detail in: Paws::SageMaker::DescribeModel

Returns: a Paws::SageMaker::DescribeModelOutput instance

Describes a model that you created using the "CreateModel" API.

Each argument is described in detail in: Paws::SageMaker::DescribeModelBiasJobDefinition

Returns: a Paws::SageMaker::DescribeModelBiasJobDefinitionResponse instance

Returns a description of a model bias job definition.

Each argument is described in detail in: Paws::SageMaker::DescribeModelExplainabilityJobDefinition

Returns: a Paws::SageMaker::DescribeModelExplainabilityJobDefinitionResponse instance

Returns a description of a model explainability job definition.

Each argument is described in detail in: Paws::SageMaker::DescribeModelPackage

Returns: a Paws::SageMaker::DescribeModelPackageOutput instance

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

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

Each argument is described in detail in: Paws::SageMaker::DescribeModelPackageGroup

Returns: a Paws::SageMaker::DescribeModelPackageGroupOutput instance

Gets a description for the specified model group.

Each argument is described in detail in: Paws::SageMaker::DescribeModelQualityJobDefinition

Returns: a Paws::SageMaker::DescribeModelQualityJobDefinitionResponse instance

Returns a description of a model quality job definition.

Each argument is described in detail in: Paws::SageMaker::DescribeMonitoringSchedule

Returns: a Paws::SageMaker::DescribeMonitoringScheduleResponse instance

Describes the schedule for a monitoring job.

Each argument is described in detail in: Paws::SageMaker::DescribeNotebookInstance

Returns: a Paws::SageMaker::DescribeNotebookInstanceOutput instance

Returns information about a notebook instance.

Each argument is described in detail in: Paws::SageMaker::DescribeNotebookInstanceLifecycleConfig

Returns: a Paws::SageMaker::DescribeNotebookInstanceLifecycleConfigOutput instance

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html).

Each argument is described in detail in: Paws::SageMaker::DescribePipeline

Returns: a Paws::SageMaker::DescribePipelineResponse instance

Describes the details of a pipeline.

Each argument is described in detail in: Paws::SageMaker::DescribePipelineDefinitionForExecution

Returns: a Paws::SageMaker::DescribePipelineDefinitionForExecutionResponse instance

Describes the details of an execution's pipeline definition.

Each argument is described in detail in: Paws::SageMaker::DescribePipelineExecution

Returns: a Paws::SageMaker::DescribePipelineExecutionResponse instance

Describes the details of a pipeline execution.

Each argument is described in detail in: Paws::SageMaker::DescribeProcessingJob

Returns: a Paws::SageMaker::DescribeProcessingJobResponse instance

Returns a description of a processing job.

Each argument is described in detail in: Paws::SageMaker::DescribeProject

Returns: a Paws::SageMaker::DescribeProjectOutput instance

Describes the details of a project.

Each argument is described in detail in: Paws::SageMaker::DescribeSubscribedWorkteam

Returns: a Paws::SageMaker::DescribeSubscribedWorkteamResponse instance

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.

Each argument is described in detail in: Paws::SageMaker::DescribeTrainingJob

Returns: a Paws::SageMaker::DescribeTrainingJobResponse instance

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.

Each argument is described in detail in: Paws::SageMaker::DescribeTransformJob

Returns: a Paws::SageMaker::DescribeTransformJobResponse instance

Returns information about a transform job.

Each argument is described in detail in: Paws::SageMaker::DescribeTrial

Returns: a Paws::SageMaker::DescribeTrialResponse instance

Provides a list of a trial's properties.

Each argument is described in detail in: Paws::SageMaker::DescribeTrialComponent

Returns: a Paws::SageMaker::DescribeTrialComponentResponse instance

Provides a list of a trials component's properties.

Each argument is described in detail in: Paws::SageMaker::DescribeUserProfile

Returns: a Paws::SageMaker::DescribeUserProfileResponse instance

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

Each argument is described in detail in: Paws::SageMaker::DescribeWorkforce

Returns: a Paws::SageMaker::DescribeWorkforceResponse instance

Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs (https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html)). Allowable IP address ranges are the IP addresses that workers can use to access tasks.

This operation applies only to private workforces.

Each argument is described in detail in: Paws::SageMaker::DescribeWorkteam

Returns: a Paws::SageMaker::DescribeWorkteamResponse instance

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).

Each argument is described in detail in: Paws::SageMaker::DisableSagemakerServicecatalogPortfolio

Returns: a Paws::SageMaker::DisableSagemakerServicecatalogPortfolioOutput instance

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

Each argument is described in detail in: Paws::SageMaker::DisassociateTrialComponent

Returns: a Paws::SageMaker::DisassociateTrialComponentResponse instance

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".

Each argument is described in detail in: Paws::SageMaker::EnableSagemakerServicecatalogPortfolio

Returns: a Paws::SageMaker::EnableSagemakerServicecatalogPortfolioOutput instance

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

Each argument is described in detail in: Paws::SageMaker::GetDeviceFleetReport

Returns: a Paws::SageMaker::GetDeviceFleetReportResponse instance

Describes a fleet.

Each argument is described in detail in: Paws::SageMaker::GetModelPackageGroupPolicy

Returns: a Paws::SageMaker::GetModelPackageGroupPolicyOutput instance

Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies (https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_identity-vs-resource.html) in the Amazon Web Services Identity and Access Management User Guide..

Each argument is described in detail in: Paws::SageMaker::GetSagemakerServicecatalogPortfolioStatus

Returns: a Paws::SageMaker::GetSagemakerServicecatalogPortfolioStatusOutput instance

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

[SuggestionQuery => Paws::SageMaker::SuggestionQuery]

Each argument is described in detail in: Paws::SageMaker::GetSearchSuggestions

Returns: a Paws::SageMaker::GetSearchSuggestionsResponse instance

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".

[ActionType => Str]
[CreatedAfter => Str]
[CreatedBefore => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[SourceUri => Str]

Each argument is described in detail in: Paws::SageMaker::ListActions

Returns: a Paws::SageMaker::ListActionsResponse instance

Lists the actions in your account and their properties.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListAlgorithms

Returns: a Paws::SageMaker::ListAlgorithmsOutput instance

Lists the machine learning algorithms that have been created.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[MaxResults => Int]
[ModifiedTimeAfter => Str]
[ModifiedTimeBefore => Str]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListAppImageConfigs

Returns: a Paws::SageMaker::ListAppImageConfigsResponse instance

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.

[DomainIdEquals => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[UserProfileNameEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListApps

Returns: a Paws::SageMaker::ListAppsResponse instance

Lists apps.

[ArtifactType => Str]
[CreatedAfter => Str]
[CreatedBefore => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[SourceUri => Str]

Each argument is described in detail in: Paws::SageMaker::ListArtifacts

Returns: a Paws::SageMaker::ListArtifactsResponse instance

Lists the artifacts in your account and their properties.

[AssociationType => Str]
[CreatedAfter => Str]
[CreatedBefore => Str]
[DestinationArn => Str]
[DestinationType => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[SourceArn => Str]
[SourceType => Str]

Each argument is described in detail in: Paws::SageMaker::ListAssociations

Returns: a Paws::SageMaker::ListAssociationsResponse instance

Lists the associations in your account and their properties.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListAutoMLJobs

Returns: a Paws::SageMaker::ListAutoMLJobsResponse instance

Request a list of jobs.

[CandidateNameEquals => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListCandidatesForAutoMLJob

Returns: a Paws::SageMaker::ListCandidatesForAutoMLJobResponse instance

List the candidates created for the job.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListCodeRepositories

Returns: a Paws::SageMaker::ListCodeRepositoriesOutput instance

Gets a list of the Git repositories in your account.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListCompilationJobs

Returns: a Paws::SageMaker::ListCompilationJobsResponse instance

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.

[ContextType => Str]
[CreatedAfter => Str]
[CreatedBefore => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[SourceUri => Str]

Each argument is described in detail in: Paws::SageMaker::ListContexts

Returns: a Paws::SageMaker::ListContextsResponse instance

Lists the contexts in your account and their properties.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[EndpointName => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListDataQualityJobDefinitions

Returns: a Paws::SageMaker::ListDataQualityJobDefinitionsResponse instance

Lists the data quality job definitions in your account.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListDeviceFleets

Returns: a Paws::SageMaker::ListDeviceFleetsResponse instance

Returns a list of devices in the fleet.

[DeviceFleetName => Str]
[LatestHeartbeatAfter => Str]
[MaxResults => Int]
[ModelName => Str]
[NextToken => Str]

Each argument is described in detail in: Paws::SageMaker::ListDevices

Returns: a Paws::SageMaker::ListDevicesResponse instance

A list of devices.

[MaxResults => Int]
[NextToken => Str]

Each argument is described in detail in: Paws::SageMaker::ListDomains

Returns: a Paws::SageMaker::ListDomainsResponse instance

Lists the domains.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[ModelNameContains => Str]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListEdgePackagingJobs

Returns: a Paws::SageMaker::ListEdgePackagingJobsResponse instance

Returns a list of edge packaging jobs.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListEndpointConfigs

Returns: a Paws::SageMaker::ListEndpointConfigsOutput instance

Lists endpoint configurations.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListEndpoints

Returns: a Paws::SageMaker::ListEndpointsOutput instance

Lists endpoints.

[CreatedAfter => Str]
[CreatedBefore => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListExperiments

Returns: a Paws::SageMaker::ListExperimentsResponse instance

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.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[FeatureGroupStatusEquals => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[OfflineStoreStatusEquals => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListFeatureGroups

Returns: a Paws::SageMaker::ListFeatureGroupsResponse instance

List "FeatureGroup"s based on given filter and order.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[MaxResults => Int]
[NextToken => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListFlowDefinitions

Returns: a Paws::SageMaker::ListFlowDefinitionsResponse instance

Returns information about the flow definitions in your account.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[MaxResults => Int]
[NextToken => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListHumanTaskUis

Returns: a Paws::SageMaker::ListHumanTaskUisResponse instance

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

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListHyperParameterTuningJobs

Returns: a Paws::SageMaker::ListHyperParameterTuningJobsResponse instance

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

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListImages

Returns: a Paws::SageMaker::ListImagesResponse instance

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.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListImageVersions

Returns: a Paws::SageMaker::ListImageVersionsResponse instance

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

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListLabelingJobs

Returns: a Paws::SageMaker::ListLabelingJobsResponse instance

Gets a list of labeling jobs.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[JobReferenceCodeContains => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListLabelingJobsForWorkteam

Returns: a Paws::SageMaker::ListLabelingJobsForWorkteamResponse instance

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

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[EndpointName => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListModelBiasJobDefinitions

Returns: a Paws::SageMaker::ListModelBiasJobDefinitionsResponse instance

Lists model bias jobs definitions that satisfy various filters.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[EndpointName => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListModelExplainabilityJobDefinitions

Returns: a Paws::SageMaker::ListModelExplainabilityJobDefinitionsResponse instance

Lists model explainability job definitions that satisfy various filters.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListModelPackageGroups

Returns: a Paws::SageMaker::ListModelPackageGroupsOutput instance

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

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[MaxResults => Int]
[ModelApprovalStatus => Str]
[ModelPackageGroupName => Str]
[ModelPackageType => Str]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListModelPackages

Returns: a Paws::SageMaker::ListModelPackagesOutput instance

Lists the model packages that have been created.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[EndpointName => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListModelQualityJobDefinitions

Returns: a Paws::SageMaker::ListModelQualityJobDefinitionsResponse instance

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

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListModels

Returns: a Paws::SageMaker::ListModelsOutput instance

Lists models created with the "CreateModel" API.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[EndpointName => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[MonitoringJobDefinitionName => Str]
[MonitoringScheduleName => Str]
[MonitoringTypeEquals => Str]
[NextToken => Str]
[ScheduledTimeAfter => Str]
[ScheduledTimeBefore => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListMonitoringExecutions

Returns: a Paws::SageMaker::ListMonitoringExecutionsResponse instance

Returns list of all monitoring job executions.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[EndpointName => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[MonitoringJobDefinitionName => Str]
[MonitoringTypeEquals => Str]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListMonitoringSchedules

Returns: a Paws::SageMaker::ListMonitoringSchedulesResponse instance

Returns list of all monitoring schedules.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListNotebookInstanceLifecycleConfigs

Returns: a Paws::SageMaker::ListNotebookInstanceLifecycleConfigsOutput instance

Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

[AdditionalCodeRepositoryEquals => Str]
[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[DefaultCodeRepositoryContains => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[NotebookInstanceLifecycleConfigNameContains => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListNotebookInstances

Returns: a Paws::SageMaker::ListNotebookInstancesOutput instance

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

[CreatedAfter => Str]
[CreatedBefore => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListPipelineExecutions

Returns: a Paws::SageMaker::ListPipelineExecutionsResponse instance

Gets a list of the pipeline executions.

[MaxResults => Int]
[NextToken => Str]
[PipelineExecutionArn => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListPipelineExecutionSteps

Returns: a Paws::SageMaker::ListPipelineExecutionStepsResponse instance

Gets a list of "PipeLineExecutionStep" objects.

[MaxResults => Int]
[NextToken => Str]

Each argument is described in detail in: Paws::SageMaker::ListPipelineParametersForExecution

Returns: a Paws::SageMaker::ListPipelineParametersForExecutionResponse instance

Gets a list of parameters for a pipeline execution.

[CreatedAfter => Str]
[CreatedBefore => Str]
[MaxResults => Int]
[NextToken => Str]
[PipelineNamePrefix => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListPipelines

Returns: a Paws::SageMaker::ListPipelinesResponse instance

Gets a list of pipelines.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListProcessingJobs

Returns: a Paws::SageMaker::ListProcessingJobsResponse instance

Lists processing jobs that satisfy various filters.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListProjects

Returns: a Paws::SageMaker::ListProjectsOutput instance

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

[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]

Each argument is described in detail in: Paws::SageMaker::ListSubscribedWorkteams

Returns: a Paws::SageMaker::ListSubscribedWorkteamsResponse instance

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.

[MaxResults => Int]
[NextToken => Str]

Each argument is described in detail in: Paws::SageMaker::ListTags

Returns: a Paws::SageMaker::ListTagsOutput instance

Returns the tags for the specified Amazon SageMaker resource.

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListTrainingJobs

Returns: a Paws::SageMaker::ListTrainingJobsResponse instance

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"

[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListTrainingJobsForHyperParameterTuningJob

Returns: a Paws::SageMaker::ListTrainingJobsForHyperParameterTuningJobResponse instance

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

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[LastModifiedTimeAfter => Str]
[LastModifiedTimeBefore => Str]
[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[StatusEquals => Str]

Each argument is described in detail in: Paws::SageMaker::ListTransformJobs

Returns: a Paws::SageMaker::ListTransformJobsResponse instance

Lists transform jobs.

[CreatedAfter => Str]
[CreatedBefore => Str]
[ExperimentName => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[SourceArn => Str]
[TrialName => Str]

Each argument is described in detail in: Paws::SageMaker::ListTrialComponents

Returns: a Paws::SageMaker::ListTrialComponentsResponse instance

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"

[CreatedAfter => Str]
[CreatedBefore => Str]
[ExperimentName => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[TrialComponentName => Str]

Each argument is described in detail in: Paws::SageMaker::ListTrials

Returns: a Paws::SageMaker::ListTrialsResponse instance

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.

[DomainIdEquals => Str]
[MaxResults => Int]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]
[UserProfileNameContains => Str]

Each argument is described in detail in: Paws::SageMaker::ListUserProfiles

Returns: a Paws::SageMaker::ListUserProfilesResponse instance

Lists user profiles.

[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListWorkforces

Returns: a Paws::SageMaker::ListWorkforcesResponse instance

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.

[MaxResults => Int]
[NameContains => Str]
[NextToken => Str]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::ListWorkteams

Returns: a Paws::SageMaker::ListWorkteamsResponse instance

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.

Each argument is described in detail in: Paws::SageMaker::PutModelPackageGroupPolicy

Returns: a Paws::SageMaker::PutModelPackageGroupPolicyOutput instance

Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies (https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_identity-vs-resource.html) in the Amazon Web Services Identity and Access Management User Guide..

[Tags => ArrayRef[Paws::SageMaker::Tag]]

Each argument is described in detail in: Paws::SageMaker::RegisterDevices

Returns: nothing

Register devices.

[HumanTaskUiArn => Str]
[UiTemplate => Paws::SageMaker::UiTemplate]

Each argument is described in detail in: Paws::SageMaker::RenderUiTemplate

Returns: a Paws::SageMaker::RenderUiTemplateResponse instance

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

[MaxResults => Int]
[NextToken => Str]
[SearchExpression => Paws::SageMaker::SearchExpression]
[SortBy => Str]
[SortOrder => Str]

Each argument is described in detail in: Paws::SageMaker::Search

Returns: a Paws::SageMaker::SearchResponse instance

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.

[ClientRequestToken => Str]
[FailureReason => Str]

Each argument is described in detail in: Paws::SageMaker::SendPipelineExecutionStepFailure

Returns: a Paws::SageMaker::SendPipelineExecutionStepFailureResponse instance

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).

[ClientRequestToken => Str]
[OutputParameters => ArrayRef[Paws::SageMaker::OutputParameter]]

Each argument is described in detail in: Paws::SageMaker::SendPipelineExecutionStepSuccess

Returns: a Paws::SageMaker::SendPipelineExecutionStepSuccessResponse instance

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).

Each argument is described in detail in: Paws::SageMaker::StartMonitoringSchedule

Returns: nothing

Starts a previously stopped monitoring schedule.

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

Each argument is described in detail in: Paws::SageMaker::StartNotebookInstance

Returns: nothing

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

[PipelineExecutionDescription => Str]
[PipelineExecutionDisplayName => Str]
[PipelineParameters => ArrayRef[Paws::SageMaker::Parameter]]

Each argument is described in detail in: Paws::SageMaker::StartPipelineExecution

Returns: a Paws::SageMaker::StartPipelineExecutionResponse instance

Starts a pipeline execution.

Each argument is described in detail in: Paws::SageMaker::StopAutoMLJob

Returns: nothing

A method for forcing the termination of a running job.

Each argument is described in detail in: Paws::SageMaker::StopCompilationJob

Returns: nothing

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".

Each argument is described in detail in: Paws::SageMaker::StopEdgePackagingJob

Returns: nothing

Request to stop an edge packaging job.

Each argument is described in detail in: Paws::SageMaker::StopHyperParameterTuningJob

Returns: nothing

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.

Each argument is described in detail in: Paws::SageMaker::StopLabelingJob

Returns: nothing

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.

Each argument is described in detail in: Paws::SageMaker::StopMonitoringSchedule

Returns: nothing

Stops a previously started monitoring schedule.

Each argument is described in detail in: Paws::SageMaker::StopNotebookInstance

Returns: nothing

Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume. Amazon SageMaker stops charging you for the ML compute instance when you call "StopNotebookInstance".

To access data on the ML storage volume for a notebook instance that has been terminated, call the "StartNotebookInstance" API. "StartNotebookInstance" launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.

Each argument is described in detail in: Paws::SageMaker::StopPipelineExecution

Returns: a Paws::SageMaker::StopPipelineExecutionResponse instance

Stops a pipeline execution.

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.

Each argument is described in detail in: Paws::SageMaker::StopProcessingJob

Returns: nothing

Stops a processing job.

Each argument is described in detail in: Paws::SageMaker::StopTrainingJob

Returns: nothing

Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the "SIGTERM" signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.

When it receives a "StopTrainingJob" request, Amazon SageMaker changes the status of the job to "Stopping". After Amazon SageMaker stops the job, it sets the status to "Stopped".

Each argument is described in detail in: Paws::SageMaker::StopTransformJob

Returns: nothing

Stops a transform job.

When Amazon SageMaker receives a "StopTransformJob" request, the status of the job changes to "Stopping". After Amazon SageMaker stops the job, the status is set to "Stopped". When you stop a transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.

[Description => Str]
[Properties => Paws::SageMaker::LineageEntityParameters]
[PropertiesToRemove => ArrayRef[Str|Undef]]
[Status => Str]

Each argument is described in detail in: Paws::SageMaker::UpdateAction

Returns: a Paws::SageMaker::UpdateActionResponse instance

Updates an action.

[KernelGatewayImageConfig => Paws::SageMaker::KernelGatewayImageConfig]

Each argument is described in detail in: Paws::SageMaker::UpdateAppImageConfig

Returns: a Paws::SageMaker::UpdateAppImageConfigResponse instance

Updates the properties of an AppImageConfig.

[ArtifactName => Str]
[Properties => Paws::SageMaker::LineageEntityParameters]
[PropertiesToRemove => ArrayRef[Str|Undef]]

Each argument is described in detail in: Paws::SageMaker::UpdateArtifact

Returns: a Paws::SageMaker::UpdateArtifactResponse instance

Updates an artifact.

[GitConfig => Paws::SageMaker::GitConfigForUpdate]

Each argument is described in detail in: Paws::SageMaker::UpdateCodeRepository

Returns: a Paws::SageMaker::UpdateCodeRepositoryOutput instance

Updates the specified Git repository with the specified values.

[Description => Str]
[Properties => Paws::SageMaker::LineageEntityParameters]
[PropertiesToRemove => ArrayRef[Str|Undef]]

Each argument is described in detail in: Paws::SageMaker::UpdateContext

Returns: a Paws::SageMaker::UpdateContextResponse instance

Updates a context.

[Description => Str]
[EnableIotRoleAlias => Bool]
[RoleArn => Str]

Each argument is described in detail in: Paws::SageMaker::UpdateDeviceFleet

Returns: nothing

Updates a fleet of devices.

Each argument is described in detail in: Paws::SageMaker::UpdateDevices

Returns: nothing

Updates one or more devices in a fleet.

[DefaultUserSettings => Paws::SageMaker::UserSettings]

Each argument is described in detail in: Paws::SageMaker::UpdateDomain

Returns: a Paws::SageMaker::UpdateDomainResponse instance

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

[DeploymentConfig => Paws::SageMaker::DeploymentConfig]
[ExcludeRetainedVariantProperties => ArrayRef[Paws::SageMaker::VariantProperty]]
[RetainAllVariantProperties => Bool]

Each argument is described in detail in: Paws::SageMaker::UpdateEndpoint

Returns: a Paws::SageMaker::UpdateEndpointOutput instance

Deploys the new "EndpointConfig" specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous "EndpointConfig" (there is no availability loss).

When Amazon SageMaker receives the request, it sets the endpoint status to "Updating". After updating the endpoint, it sets the status to "InService". To check the status of an endpoint, use the DescribeEndpoint API.

You must not delete an "EndpointConfig" in use by an endpoint that is live or while the "UpdateEndpoint" or "CreateEndpoint" operations are being performed on the endpoint. To update an endpoint, you must create a new "EndpointConfig".

If you delete the "EndpointConfig" of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.

Each argument is described in detail in: Paws::SageMaker::UpdateEndpointWeightsAndCapacities

Returns: a Paws::SageMaker::UpdateEndpointWeightsAndCapacitiesOutput instance

Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, Amazon SageMaker sets the endpoint status to "Updating". After updating the endpoint, it sets the status to "InService". To check the status of an endpoint, use the DescribeEndpoint API.

[Description => Str]
[DisplayName => Str]

Each argument is described in detail in: Paws::SageMaker::UpdateExperiment

Returns: a Paws::SageMaker::UpdateExperimentResponse instance

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

[DeleteProperties => ArrayRef[Str|Undef]]
[Description => Str]
[DisplayName => Str]
[RoleArn => Str]

Each argument is described in detail in: Paws::SageMaker::UpdateImage

Returns: a Paws::SageMaker::UpdateImageResponse instance

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

[ApprovalDescription => Str]

Each argument is described in detail in: Paws::SageMaker::UpdateModelPackage

Returns: a Paws::SageMaker::UpdateModelPackageOutput instance

Updates a versioned model.

Each argument is described in detail in: Paws::SageMaker::UpdateMonitoringSchedule

Returns: a Paws::SageMaker::UpdateMonitoringScheduleResponse instance

Updates a previously created schedule.

[AcceleratorTypes => ArrayRef[Str|Undef]]
[AdditionalCodeRepositories => ArrayRef[Str|Undef]]
[DefaultCodeRepository => Str]
[DisassociateAcceleratorTypes => Bool]
[DisassociateAdditionalCodeRepositories => Bool]
[DisassociateDefaultCodeRepository => Bool]
[DisassociateLifecycleConfig => Bool]
[InstanceType => Str]
[LifecycleConfigName => Str]
[RoleArn => Str]
[RootAccess => Str]
[VolumeSizeInGB => Int]

Each argument is described in detail in: Paws::SageMaker::UpdateNotebookInstance

Returns: a Paws::SageMaker::UpdateNotebookInstanceOutput instance

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.

[OnCreate => ArrayRef[Paws::SageMaker::NotebookInstanceLifecycleHook]]
[OnStart => ArrayRef[Paws::SageMaker::NotebookInstanceLifecycleHook]]

Each argument is described in detail in: Paws::SageMaker::UpdateNotebookInstanceLifecycleConfig

Returns: a Paws::SageMaker::UpdateNotebookInstanceLifecycleConfigOutput instance

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

[PipelineDefinition => Str]
[PipelineDescription => Str]
[PipelineDisplayName => Str]
[RoleArn => Str]

Each argument is described in detail in: Paws::SageMaker::UpdatePipeline

Returns: a Paws::SageMaker::UpdatePipelineResponse instance

Updates a pipeline.

[PipelineExecutionDescription => Str]
[PipelineExecutionDisplayName => Str]

Each argument is described in detail in: Paws::SageMaker::UpdatePipelineExecution

Returns: a Paws::SageMaker::UpdatePipelineExecutionResponse instance

Updates a pipeline execution.

[ProfilerConfig => Paws::SageMaker::ProfilerConfigForUpdate]
[ProfilerRuleConfigurations => ArrayRef[Paws::SageMaker::ProfilerRuleConfiguration]]

Each argument is described in detail in: Paws::SageMaker::UpdateTrainingJob

Returns: a Paws::SageMaker::UpdateTrainingJobResponse instance

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

[DisplayName => Str]

Each argument is described in detail in: Paws::SageMaker::UpdateTrial

Returns: a Paws::SageMaker::UpdateTrialResponse instance

Updates the display name of a trial.

[DisplayName => Str]
[EndTime => Str]
[InputArtifacts => Paws::SageMaker::TrialComponentArtifacts]
[InputArtifactsToRemove => ArrayRef[Str|Undef]]
[OutputArtifacts => Paws::SageMaker::TrialComponentArtifacts]
[OutputArtifactsToRemove => ArrayRef[Str|Undef]]
[Parameters => Paws::SageMaker::TrialComponentParameters]
[ParametersToRemove => ArrayRef[Str|Undef]]
[StartTime => Str]
[Status => Paws::SageMaker::TrialComponentStatus]

Each argument is described in detail in: Paws::SageMaker::UpdateTrialComponent

Returns: a Paws::SageMaker::UpdateTrialComponentResponse instance

Updates one or more properties of a trial component.

[UserSettings => Paws::SageMaker::UserSettings]

Each argument is described in detail in: Paws::SageMaker::UpdateUserProfile

Returns: a Paws::SageMaker::UpdateUserProfileResponse instance

Updates a user profile.

[OidcConfig => Paws::SageMaker::OidcConfig]
[SourceIpConfig => Paws::SageMaker::SourceIpConfig]

Each argument is described in detail in: Paws::SageMaker::UpdateWorkforce

Returns: a Paws::SageMaker::UpdateWorkforceResponse instance

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.

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 (https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html). 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.

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.

[Description => Str]
[MemberDefinitions => ArrayRef[Paws::SageMaker::MemberDefinition]]
[NotificationConfiguration => Paws::SageMaker::NotificationConfiguration]

Each argument is described in detail in: Paws::SageMaker::UpdateWorkteam

Returns: a Paws::SageMaker::UpdateWorkteamResponse instance

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

Paginator methods are helpers that repetively call methods that return partial results

If passed a sub as first parameter, it will call the sub for each element found in :

 - ActionSummaries, passing the object as the first parameter, and the string 'ActionSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListActionsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - AlgorithmSummaryList, passing the object as the first parameter, and the string 'AlgorithmSummaryList' as the second parameter

If not, it will return a a Paws::SageMaker::ListAlgorithmsOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - AppImageConfigs, passing the object as the first parameter, and the string 'AppImageConfigs' as the second parameter

If not, it will return a a Paws::SageMaker::ListAppImageConfigsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Apps, passing the object as the first parameter, and the string 'Apps' as the second parameter

If not, it will return a a Paws::SageMaker::ListAppsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - ArtifactSummaries, passing the object as the first parameter, and the string 'ArtifactSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListArtifactsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - AssociationSummaries, passing the object as the first parameter, and the string 'AssociationSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListAssociationsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - AutoMLJobSummaries, passing the object as the first parameter, and the string 'AutoMLJobSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListAutoMLJobsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Candidates, passing the object as the first parameter, and the string 'Candidates' as the second parameter

If not, it will return a a Paws::SageMaker::ListCandidatesForAutoMLJobResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - CodeRepositorySummaryList, passing the object as the first parameter, and the string 'CodeRepositorySummaryList' as the second parameter

If not, it will return a a Paws::SageMaker::ListCodeRepositoriesOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - CompilationJobSummaries, passing the object as the first parameter, and the string 'CompilationJobSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListCompilationJobsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - ContextSummaries, passing the object as the first parameter, and the string 'ContextSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListContextsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - JobDefinitionSummaries, passing the object as the first parameter, and the string 'JobDefinitionSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListDataQualityJobDefinitionsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - DeviceFleetSummaries, passing the object as the first parameter, and the string 'DeviceFleetSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListDeviceFleetsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - DeviceSummaries, passing the object as the first parameter, and the string 'DeviceSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListDevicesResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Domains, passing the object as the first parameter, and the string 'Domains' as the second parameter

If not, it will return a a Paws::SageMaker::ListDomainsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - EdgePackagingJobSummaries, passing the object as the first parameter, and the string 'EdgePackagingJobSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListEdgePackagingJobsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - EndpointConfigs, passing the object as the first parameter, and the string 'EndpointConfigs' as the second parameter

If not, it will return a a Paws::SageMaker::ListEndpointConfigsOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Endpoints, passing the object as the first parameter, and the string 'Endpoints' as the second parameter

If not, it will return a a Paws::SageMaker::ListEndpointsOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - ExperimentSummaries, passing the object as the first parameter, and the string 'ExperimentSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListExperimentsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - FeatureGroupSummaries, passing the object as the first parameter, and the string 'FeatureGroupSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListFeatureGroupsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - FlowDefinitionSummaries, passing the object as the first parameter, and the string 'FlowDefinitionSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListFlowDefinitionsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - HumanTaskUiSummaries, passing the object as the first parameter, and the string 'HumanTaskUiSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListHumanTaskUisResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - HyperParameterTuningJobSummaries, passing the object as the first parameter, and the string 'HyperParameterTuningJobSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListHyperParameterTuningJobsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Images, passing the object as the first parameter, and the string 'Images' as the second parameter

If not, it will return a a Paws::SageMaker::ListImagesResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - ImageVersions, passing the object as the first parameter, and the string 'ImageVersions' as the second parameter

If not, it will return a a Paws::SageMaker::ListImageVersionsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - LabelingJobSummaryList, passing the object as the first parameter, and the string 'LabelingJobSummaryList' as the second parameter

If not, it will return a a Paws::SageMaker::ListLabelingJobsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - LabelingJobSummaryList, passing the object as the first parameter, and the string 'LabelingJobSummaryList' as the second parameter

If not, it will return a a Paws::SageMaker::ListLabelingJobsForWorkteamResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - JobDefinitionSummaries, passing the object as the first parameter, and the string 'JobDefinitionSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListModelBiasJobDefinitionsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - JobDefinitionSummaries, passing the object as the first parameter, and the string 'JobDefinitionSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListModelExplainabilityJobDefinitionsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - ModelPackageGroupSummaryList, passing the object as the first parameter, and the string 'ModelPackageGroupSummaryList' as the second parameter

If not, it will return a a Paws::SageMaker::ListModelPackageGroupsOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - ModelPackageSummaryList, passing the object as the first parameter, and the string 'ModelPackageSummaryList' as the second parameter

If not, it will return a a Paws::SageMaker::ListModelPackagesOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - JobDefinitionSummaries, passing the object as the first parameter, and the string 'JobDefinitionSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListModelQualityJobDefinitionsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Models, passing the object as the first parameter, and the string 'Models' as the second parameter

If not, it will return a a Paws::SageMaker::ListModelsOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - MonitoringExecutionSummaries, passing the object as the first parameter, and the string 'MonitoringExecutionSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListMonitoringExecutionsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - MonitoringScheduleSummaries, passing the object as the first parameter, and the string 'MonitoringScheduleSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListMonitoringSchedulesResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - NotebookInstanceLifecycleConfigs, passing the object as the first parameter, and the string 'NotebookInstanceLifecycleConfigs' as the second parameter

If not, it will return a a Paws::SageMaker::ListNotebookInstanceLifecycleConfigsOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - NotebookInstances, passing the object as the first parameter, and the string 'NotebookInstances' as the second parameter

If not, it will return a a Paws::SageMaker::ListNotebookInstancesOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - PipelineExecutionSummaries, passing the object as the first parameter, and the string 'PipelineExecutionSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListPipelineExecutionsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - PipelineExecutionSteps, passing the object as the first parameter, and the string 'PipelineExecutionSteps' as the second parameter

If not, it will return a a Paws::SageMaker::ListPipelineExecutionStepsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - PipelineParameters, passing the object as the first parameter, and the string 'PipelineParameters' as the second parameter

If not, it will return a a Paws::SageMaker::ListPipelineParametersForExecutionResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - PipelineSummaries, passing the object as the first parameter, and the string 'PipelineSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListPipelinesResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - ProcessingJobSummaries, passing the object as the first parameter, and the string 'ProcessingJobSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListProcessingJobsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - SubscribedWorkteams, passing the object as the first parameter, and the string 'SubscribedWorkteams' as the second parameter

If not, it will return a a Paws::SageMaker::ListSubscribedWorkteamsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Tags, passing the object as the first parameter, and the string 'Tags' as the second parameter

If not, it will return a a Paws::SageMaker::ListTagsOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - TrainingJobSummaries, passing the object as the first parameter, and the string 'TrainingJobSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListTrainingJobsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - TrainingJobSummaries, passing the object as the first parameter, and the string 'TrainingJobSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListTrainingJobsForHyperParameterTuningJobResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - TransformJobSummaries, passing the object as the first parameter, and the string 'TransformJobSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListTransformJobsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - TrialComponentSummaries, passing the object as the first parameter, and the string 'TrialComponentSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListTrialComponentsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - TrialSummaries, passing the object as the first parameter, and the string 'TrialSummaries' as the second parameter

If not, it will return a a Paws::SageMaker::ListTrialsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - UserProfiles, passing the object as the first parameter, and the string 'UserProfiles' as the second parameter

If not, it will return a a Paws::SageMaker::ListUserProfilesResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Workforces, passing the object as the first parameter, and the string 'Workforces' as the second parameter

If not, it will return a a Paws::SageMaker::ListWorkforcesResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Workteams, passing the object as the first parameter, and the string 'Workteams' as the second parameter

If not, it will return a a Paws::SageMaker::ListWorkteamsResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Results, passing the object as the first parameter, and the string 'Results' as the second parameter

If not, it will return a a Paws::SageMaker::SearchResponse instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

This service class forms part of Paws

The source code is located here: <https://github.com/pplu/aws-sdk-perl>

Please report bugs to: <https://github.com/pplu/aws-sdk-perl/issues>

2022-06-01 perl v5.40.2
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