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

Definition of the public APIs exposed by SageMaker

For the AWS API documentation, see https://docs.aws.amazon.com/goto/WebAPI/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, models, 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 Using Cost Allocation Tags (http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what) in the AWS Billing and Cost Management User Guide.

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 (http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpointConfig.html) API.

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

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 (http://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 (http://docs.aws.amazon.com/sagemaker/latest/dg/ex1.html).

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 (http://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
TrainingJobDefinition => Paws::SageMaker::HyperParameterTrainingJobDefinition
[Tags => ArrayRef[Paws::SageMaker::Tag]]

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

Returns: a Paws::SageMaker::CreateHyperParameterTuningJobResponse instance

Starts a hyperparameter tuning job.

CreateModel

ExecutionRoleArn => Str
ModelName => Str
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 one or more containers. For each 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 into production.

Use this API to create a model only if you want to use Amazon SageMaker hosting services. 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.

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. 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.

CreateNotebookInstance

InstanceType => Str
NotebookInstanceName => Str
RoleArn => Str
[DirectInternetAccess => Str]
[KmsKeyId => Str]
[LifecycleConfigName => Str]
[SecurityGroupIds => ArrayRef[Str|Undef]]
[SubnetId => Str]
[Tags => ArrayRef[Paws::SageMaker::Tag]]

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 (http://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 notebook-lifecycle-config.

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.

CreateTrainingJob

AlgorithmSpecification => Paws::SageMaker::AlgorithmSpecification
InputDataConfig => ArrayRef[Paws::SageMaker::Channel]
OutputDataConfig => Paws::SageMaker::OutputDataConfig
ResourceConfig => Paws::SageMaker::ResourceConfig
RoleArn => Str
StoppingCondition => Paws::SageMaker::StoppingCondition
TrainingJobName => Str
[HyperParameters => Paws::SageMaker::HyperParameters]
[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 deep 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 influence the quality of the final model. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms (http://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 duration for training. Use this parameter to cap model training costs.

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

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.

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 (http://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.

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.

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.

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.

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.

For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.

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.

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 objects that describe the hyperparameter tuning jobs launched in your account.

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 (http://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 API.

ListNotebookInstances

[CreationTimeAfter => Str]
[CreationTimeBefore => 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.

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 objects that describe the training jobs that a hyperparameter tuning job launched.

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.

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 toAmazon 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.

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.

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.

Training algorithms provided by Amazon SageMaker save the intermediate results of a model training job. This intermediate data is a valid model artifact. You can use the model artifacts that are saved when Amazon SageMaker stops a training job to create a model.

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.

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

UpdateNotebookInstance

NotebookInstanceName => Str
[InstanceType => Str]
[RoleArn => Str]

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. You can also update the VPC security groups.

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 API.

PAGINATORS

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