NAME

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

SYNOPSIS

  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' } ],
  );

DESCRIPTION

Provides APIs for creating and managing Amazon SageMaker resources.

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

METHODS

AddTags

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

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 AWS 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

CreateAlgorithm

AlgorithmName => Str
TrainingSpecification => Paws::SageMaker::TrainingSpecification
[AlgorithmDescription => Str]
[CertifyForMarketplace => Bool]
[InferenceSpecification => Paws::SageMaker::InferenceSpecification]
[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 AWS Marketplace.

CreateCodeRepository

CodeRepositoryName => Str
GitConfig => Paws::SageMaker::GitConfig

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 AWS CodeCommit (http://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html) or in any other Git repository.

CreateCompilationJob

CompilationJobName => Str
InputConfig => Paws::SageMaker::InputConfig
OutputConfig => Paws::SageMaker::OutputConfig
RoleArn => Str
StoppingCondition => Paws::SageMaker::StoppingCondition

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 AWS IoT Greengrass. In that case, deploy them as an ML resource.

In the request body, you provide the following:

A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job

You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.

To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

CreateEndpoint

EndpointConfigName => Str
EndpointName => Str
[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 (https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpointConfig.html) API.

Use this API only for hosting models using Amazon SageMaker hosting services.

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.

The endpoint name must be unique within an AWS Region in your AWS account.

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

When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint (https://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html) API.

For an example, see Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/ex1.html).

If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region (http://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html) in the AWS Identity and Access Management User Guide.

CreateEndpointConfig

EndpointConfigName => Str
ProductionVariants => ArrayRef[Paws::SageMaker::ProductionVariant]
[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 (https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpoint.html) API.

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

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

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

CreateHyperParameterTuningJob

HyperParameterTuningJobConfig => Paws::SageMaker::HyperParameterTuningJobConfig
HyperParameterTuningJobName => Str
[Tags => ArrayRef[Paws::SageMaker::Tag]]
[TrainingJobDefinition => 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.

CreateLabelingJob

HumanTaskConfig => Paws::SageMaker::HumanTaskConfig
InputConfig => Paws::SageMaker::LabelingJobInputConfig
LabelAttributeName => Str
LabelingJobName => Str
OutputConfig => Paws::SageMaker::LabelingJobOutputConfig
RoleArn => Str
[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 AWS Marketplace. Vendors provide expertise in specific areas.
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.

You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling (http://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 (http://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.

CreateModel

ExecutionRoleArn => Str
ModelName => Str
[Containers => ArrayRef[Paws::SageMaker::ContainerDefinition]]
[EnableNetworkIsolation => Bool]
[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 containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model for predictions.

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

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

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

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

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

CreateModelPackage

ModelPackageName => Str
[CertifyForMarketplace => Bool]
[InferenceSpecification => Paws::SageMaker::InferenceSpecification]
[ModelPackageDescription => Str]
[SourceAlgorithmSpecification => Paws::SageMaker::SourceAlgorithmSpecification]
[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 AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.

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

CreateNotebookInstance

InstanceType => Str
NotebookInstanceName => Str
RoleArn => Str
[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:

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

After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).

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

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

CreateNotebookInstanceLifecycleConfig

NotebookInstanceLifecycleConfigName => Str
[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).

CreatePresignedNotebookInstanceUrl

NotebookInstanceName => Str
[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.

IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.For example, 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/nbi-ip-filter.html).

The URL that you get from a call to is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.

CreateTrainingJob

AlgorithmSpecification => Paws::SageMaker::AlgorithmSpecification
OutputDataConfig => Paws::SageMaker::OutputDataConfig
ResourceConfig => Paws::SageMaker::ResourceConfig
RoleArn => Str
StoppingCondition => Paws::SageMaker::StoppingCondition
TrainingJobName => Str
[EnableInterContainerTrafficEncryption => Bool]
[EnableNetworkIsolation => Bool]
[HyperParameters => Paws::SageMaker::HyperParameters]
[InputDataConfig => ArrayRef[Paws::SageMaker::Channel]]
[Tags => ArrayRef[Paws::SageMaker::Tag]]
[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 inferences.

In the request body, you provide the following:

AlgorithmSpecification - Identifies the training algorithm to use.
HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).
InputDataConfig - Describes the training dataset and the Amazon S3 location where it is stored.
OutputDataConfig - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training.
ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.
RoleARN - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training.
StoppingCondition - Sets a time limit for training. Use this parameter to cap model training costs.

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

CreateTransformJob

ModelName => Str
TransformInput => Paws::SageMaker::TransformInput
TransformJobName => Str
TransformOutput => Paws::SageMaker::TransformOutput
TransformResources => Paws::SageMaker::TransformResources
[BatchStrategy => Str]
[DataProcessing => Paws::SageMaker::DataProcessing]
[Environment => Paws::SageMaker::TransformEnvironmentMap]
[MaxConcurrentTransforms => Int]
[MaxPayloadInMB => Int]
[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 AWS Region in an AWS account.
ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see CreateModel.
TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.
TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
TransformResources - Identifies the ML compute instances for the transform job.

For more information about how batch transformation works Amazon SageMaker, see How It Works (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html).

CreateWorkteam

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

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.

DeleteAlgorithm

AlgorithmName => Str

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

Returns: nothing

Removes the specified algorithm from your account.

DeleteCodeRepository

CodeRepositoryName => Str

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

Returns: nothing

Deletes the specified Git repository from your account.

DeleteEndpoint

EndpointName => Str

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.

DeleteEndpointConfig

EndpointConfigName => Str

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.

DeleteModel

ModelName => Str

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateModel.html) API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.

DeleteModelPackage

ModelPackageName => Str

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 AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.

DeleteNotebookInstance

NotebookInstanceName => Str

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.

DeleteNotebookInstanceLifecycleConfig

NotebookInstanceLifecycleConfigName => Str

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

Returns: nothing

Deletes a notebook instance lifecycle configuration.

DeleteTags

ResourceArn => Str
TagKeys => ArrayRef[Str|Undef]

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.

DeleteWorkteam

WorkteamName => Str

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.

DescribeAlgorithm

AlgorithmName => Str

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.

DescribeCodeRepository

CodeRepositoryName => Str

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

Returns: a Paws::SageMaker::DescribeCodeRepositoryOutput instance

Gets details about the specified Git repository.

DescribeCompilationJob

CompilationJobName => Str

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.

DescribeEndpoint

EndpointName => Str

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

Returns: a Paws::SageMaker::DescribeEndpointOutput instance

Returns the description of an endpoint.

DescribeEndpointConfig

EndpointConfigName => Str

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.

DescribeHyperParameterTuningJob

HyperParameterTuningJobName => Str

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

Returns: a Paws::SageMaker::DescribeHyperParameterTuningJobResponse instance

Gets a description of a hyperparameter tuning job.

DescribeLabelingJob

LabelingJobName => Str

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

Returns: a Paws::SageMaker::DescribeLabelingJobResponse instance

Gets information about a labeling job.

DescribeModel

ModelName => Str

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.

DescribeModelPackage

ModelPackageName => Str

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 AWS Marketplace.

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

DescribeNotebookInstance

NotebookInstanceName => Str

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

Returns: a Paws::SageMaker::DescribeNotebookInstanceOutput instance

Returns information about a notebook instance.

DescribeNotebookInstanceLifecycleConfig

NotebookInstanceLifecycleConfigName => Str

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.

DescribeSubscribedWorkteam

WorkteamArn => Str

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 AWS Marketplace.

DescribeTrainingJob

TrainingJobName => Str

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

Returns: a Paws::SageMaker::DescribeTrainingJobResponse instance

Returns information about a training job.

DescribeTransformJob

TransformJobName => Str

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

Returns: a Paws::SageMaker::DescribeTransformJobResponse instance

Returns information about a transform job.

DescribeWorkteam

WorkteamName => Str

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).

GetSearchSuggestions

Resource => Str
[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.

ListAlgorithms

[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.

ListCodeRepositories

[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.

ListCompilationJobs

[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.

ListEndpointConfigs

[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.

ListEndpoints

[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.

ListHyperParameterTuningJobs

[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.

ListLabelingJobs

[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.

ListLabelingJobsForWorkteam

WorkteamArn => Str
[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.

ListModelPackages

[CreationTimeAfter => Str]
[CreationTimeBefore => Str]
[MaxResults => Int]
[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.

ListModels

[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 (https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateModel.html) API.

ListNotebookInstanceLifecycleConfigs

[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.

ListNotebookInstances

[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 AWS Region.

ListSubscribedWorkteams

[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 AWS Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

ResourceArn => Str
[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.

ListTrainingJobs

[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.

ListTrainingJobsForHyperParameterTuningJob

HyperParameterTuningJobName => Str
[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.

ListTransformJobs

[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.

ListWorkteams

[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 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.

RenderUiTemplate

RoleArn => Str
Task => Paws::SageMaker::RenderableTask
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.

Search

Resource => Str
[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 resource objects are returned as a list of SearchResult 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: numerical, text, Booleans, and timestamps.

StartNotebookInstance

NotebookInstanceName => Str

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.

StopCompilationJob

CompilationJobName => Str

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.

StopHyperParameterTuningJob

HyperParameterTuningJobName => Str

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.

StopLabelingJob

LabelingJobName => Str

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.

StopNotebookInstance

NotebookInstanceName => Str

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.

StopTrainingJob

TrainingJobName => Str

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.

StopTransformJob

TransformJobName => Str

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.

UpdateCodeRepository

CodeRepositoryName => Str
[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.

UpdateEndpoint

EndpointConfigName => Str
EndpointName => Str

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html) API.

UpdateEndpointWeightsAndCapacities

DesiredWeightsAndCapacities => ArrayRef[Paws::SageMaker::DesiredWeightAndCapacity]
EndpointName => Str

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html) API.

UpdateNotebookInstance

NotebookInstanceName => Str
[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.

UpdateNotebookInstanceLifecycleConfig

NotebookInstanceLifecycleConfigName => Str
[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.

UpdateWorkteam

WorkteamName => Str
[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.

PAGINATORS

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