AWS Tools for Windows PowerShell
Command Reference

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Synopsis

Calls the Amazon SageMaker Service CreateHyperParameterTuningJob API operation.

Syntax

New-SMHyperParameterTuningJob
-HyperParameterTuningJobName <String>
-ParameterRanges_CategoricalParameterRange <CategoricalParameterRange[]>
-ParameterRanges_ContinuousParameterRange <ContinuousParameterRange[]>
-TrainingJobDefinition_InputDataConfig <Channel[]>
-ParameterRanges_IntegerParameterRange <IntegerParameterRange[]>
-ResourceLimits_MaxNumberOfTrainingJob <Int32>
-ResourceLimits_MaxParallelTrainingJob <Int32>
-StoppingCondition_MaxRuntimeInSecond <Int32>
-AlgorithmSpecification_MetricDefinition <MetricDefinition[]>
-HyperParameterTuningJobObjective_MetricName <String>
-TrainingJobDefinition_OutputDataConfig <OutputDataConfig>
-TrainingJobDefinition_ResourceConfig <ResourceConfig>
-TrainingJobDefinition_RoleArn <String>
-VpcConfig_SecurityGroupId <String[]>
-TrainingJobDefinition_StaticHyperParameter <Hashtable>
-HyperParameterTuningJobConfig_Strategy <HyperParameterTuningJobStrategyType>
-VpcConfig_Subnet <String[]>
-Tag <Tag[]>
-AlgorithmSpecification_TrainingImage <String>
-AlgorithmSpecification_TrainingInputMode <TrainingInputMode>
-HyperParameterTuningJobObjective_Type <HyperParameterTuningJobObjectiveType>
-Force <SwitchParameter>

Description

Starts a hyperparameter tuning job.

Parameters

-AlgorithmSpecification_MetricDefinition <MetricDefinition[]>
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Required?False
Position?Named
Accept pipeline input?False
-AlgorithmSpecification_TrainingImage <String>
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see sagemaker-algo-docker-registry-paths.
Required?False
Position?Named
Accept pipeline input?False
-AlgorithmSpecification_TrainingInputMode <TrainingInputMode>
The input mode that the algorithm supports: File or Pipe. In File input mode, Amazon SageMaker downloads the training data from Amazon S3 to the storage volume that is attached to the training instance and mounts the directory to the Docker volume for the training container. In Pipe input mode, Amazon SageMaker streams data directly from Amazon S3 to the container. If you specify File mode, make sure that you provision the storage volume that is attached to the training instance with enough capacity to accommodate the training data downloaded from Amazon S3, the model artifacts, and intermediate information.For more information about input modes, see Algorithms.
Required?False
Position?Named
Accept pipeline input?False
-Force <SwitchParameter>
This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution.
Required?False
Position?Named
Accept pipeline input?False
-HyperParameterTuningJobConfig_Strategy <HyperParameterTuningJobStrategyType>
Specifies the search strategy for hyperparameters. Currently, the only valid value is Bayesian.
Required?False
Position?Named
Accept pipeline input?False
-HyperParameterTuningJobName <String>
The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same AWS account and AWS Region. Names are not case sensitive, and must be between 1-32 characters.
Required?False
Position?1
Accept pipeline input?True (ByValue, )
-HyperParameterTuningJobObjective_MetricName <String>
The name of the metric to use for the objective metric.
Required?False
Position?Named
Accept pipeline input?False
-HyperParameterTuningJobObjective_Type <HyperParameterTuningJobObjectiveType>
Whether to minimize or maximize the objective metric.
Required?False
Position?Named
Accept pipeline input?False
-ParameterRanges_CategoricalParameterRange <CategoricalParameterRange[]>
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Required?False
Position?Named
Accept pipeline input?False
-ParameterRanges_ContinuousParameterRange <ContinuousParameterRange[]>
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Required?False
Position?Named
Accept pipeline input?False
-ParameterRanges_IntegerParameterRange <IntegerParameterRange[]>
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Required?False
Position?Named
Accept pipeline input?False
-ResourceLimits_MaxNumberOfTrainingJob <Int32>
The maximum number of training jobs that a hyperparameter tuning job can launch.
Required?False
Position?Named
Accept pipeline input?False
-ResourceLimits_MaxParallelTrainingJob <Int32>
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
Required?False
Position?Named
Accept pipeline input?False
-StoppingCondition_MaxRuntimeInSecond <Int32>
The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 5 days.
Required?False
Position?Named
Accept pipeline input?False
-Tag <Tag[]>
An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Required?False
Position?Named
Accept pipeline input?False
-TrainingJobDefinition_InputDataConfig <Channel[]>
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
Required?False
Position?Named
Accept pipeline input?False
-TrainingJobDefinition_OutputDataConfig <OutputDataConfig>
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
Required?False
Position?Named
Accept pipeline input?False
-TrainingJobDefinition_ResourceConfig <ResourceConfig>
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Required?False
Position?Named
Accept pipeline input?False
-TrainingJobDefinition_RoleArn <String>
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
Required?False
Position?Named
Accept pipeline input?False
-TrainingJobDefinition_StaticHyperParameter <Hashtable>
Specifies the values of hyperparameters that do not change for the tuning job.
Required?False
Position?Named
Accept pipeline input?False
-VpcConfig_SecurityGroupId <String[]>
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
Required?False
Position?Named
Accept pipeline input?False
-VpcConfig_Subnet <String[]>
The ID of the subnets in the VPC to which you want to connect your training job or model.
Required?False
Position?Named
Accept pipeline input?False

Common Credential and Region Parameters

-AccessKey <String>
The AWS access key for the user account. This can be a temporary access key if the corresponding session token is supplied to the -SessionToken parameter.
Required? False
Position? Named
Accept pipeline input? False
-Credential <AWSCredentials>
An AWSCredentials object instance containing access and secret key information, and optionally a token for session-based credentials.
Required? False
Position? Named
Accept pipeline input? False
-ProfileLocation <String>

Used to specify the name and location of the ini-format credential file (shared with the AWS CLI and other AWS SDKs)

If this optional parameter is omitted this cmdlet will search the encrypted credential file used by the AWS SDK for .NET and AWS Toolkit for Visual Studio first. If the profile is not found then the cmdlet will search in the ini-format credential file at the default location: (user's home directory)\.aws\credentials. Note that the encrypted credential file is not supported on all platforms. It will be skipped when searching for profiles on Windows Nano Server, Mac, and Linux platforms.

If this parameter is specified then this cmdlet will only search the ini-format credential file at the location given.

As the current folder can vary in a shell or during script execution it is advised that you use specify a fully qualified path instead of a relative path.

Required? False
Position? Named
Accept pipeline input? False
-ProfileName <String>
The user-defined name of an AWS credentials or SAML-based role profile containing credential information. The profile is expected to be found in the secure credential file shared with the AWS SDK for .NET and AWS Toolkit for Visual Studio. You can also specify the name of a profile stored in the .ini-format credential file used with the AWS CLI and other AWS SDKs.
Required? False
Position? Named
Accept pipeline input? False
-NetworkCredential <PSCredential>
Used with SAML-based authentication when ProfileName references a SAML role profile. Contains the network credentials to be supplied during authentication with the configured identity provider's endpoint. This parameter is not required if the user's default network identity can or should be used during authentication.
Required? False
Position? Named
Accept pipeline input? False
-SecretKey <String>
The AWS secret key for the user account. This can be a temporary secret key if the corresponding session token is supplied to the -SessionToken parameter.
Required? False
Position? Named
Accept pipeline input? False
-SessionToken <String>
The session token if the access and secret keys are temporary session-based credentials.
Required? False
Position? Named
Accept pipeline input? False
-Region <String>
The system name of the AWS region in which the operation should be invoked. For example, us-east-1, eu-west-1 etc.
Required? False
Position? Named
Accept pipeline input? False
-EndpointUrl <String>

The endpoint to make the call against.

Note: This parameter is primarily for internal AWS use and is not required/should not be specified for normal usage. The cmdlets normally determine which endpoint to call based on the region specified to the -Region parameter or set as default in the shell (via Set-DefaultAWSRegion). Only specify this parameter if you must direct the call to a specific custom endpoint.

Required? False
Position? Named
Accept pipeline input? False

Inputs

You can pipe a String object to this cmdlet for the HyperParameterTuningJobName parameter.

Outputs

This cmdlet returns a String object. The service call response (type Amazon.SageMaker.Model.CreateHyperParameterTuningJobResponse) can also be referenced from properties attached to the cmdlet entry in the $AWSHistory stack.

Supported Version

AWS Tools for PowerShell: 2.x.y.z