AWS Tools for Windows PowerShell
Command Reference

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

Synopsis

Calls the Amazon SageMaker Service CreateHyperParameterTuningJob API operation.

Syntax

New-SMHyperParameterTuningJob
-HyperParameterTuningJobName <String>
-AlgorithmSpecification_AlgorithmName <String>
-HyperParameterTuningResourceConfig_AllocationStrategy <HyperParameterTuningAllocationStrategy>
-ParameterRanges_AutoParameter <AutoParameter[]>
-HyperParameterRanges_AutoParameter <AutoParameter[]>
-ParameterRanges_CategoricalParameterRange <CategoricalParameterRange[]>
-HyperParameterRanges_CategoricalParameterRange <CategoricalParameterRange[]>
-ConvergenceDetected_CompleteOnConvergence <CompleteOnConvergence>
-ParameterRanges_ContinuousParameterRange <ContinuousParameterRange[]>
-HyperParameterRanges_ContinuousParameterRange <ContinuousParameterRange[]>
-TrainingJobDefinition_DefinitionName <String>
-TrainingJobDefinition_EnableInterContainerTrafficEncryption <Boolean>
-TrainingJobDefinition_EnableManagedSpotTraining <Boolean>
-TrainingJobDefinition_EnableNetworkIsolation <Boolean>
-TrainingJobDefinition_Environment <Hashtable>
-TrainingJobDefinition_InputDataConfig <Channel[]>
-HyperParameterTuningResourceConfig_InstanceConfig <HyperParameterTuningInstanceConfig[]>
-HyperParameterTuningResourceConfig_InstanceCount <Int32>
-HyperParameterTuningResourceConfig_InstanceType <TrainingInstanceType>
-ParameterRanges_IntegerParameterRange <IntegerParameterRange[]>
-HyperParameterRanges_IntegerParameterRange <IntegerParameterRange[]>
-CheckpointConfig_LocalPath <String>
-RetryStrategy_MaximumRetryAttempt <Int32>
-ResourceLimits_MaxNumberOfTrainingJob <Int32>
-BestObjectiveNotImproving_MaxNumberOfTrainingJobsNotImproving <Int32>
-ResourceLimits_MaxParallelTrainingJob <Int32>
-StoppingCondition_MaxPendingTimeInSecond <Int32>
-HyperbandStrategyConfig_MaxResource <Int32>
-ResourceLimits_MaxRuntimeInSecond <Int32>
-StoppingCondition_MaxRuntimeInSecond <Int32>
-StoppingCondition_MaxWaitTimeInSecond <Int32>
-AlgorithmSpecification_MetricDefinition <MetricDefinition[]>
-HyperParameterTuningJobObjective_MetricName <String>
-TuningObjective_MetricName <String>
-HyperbandStrategyConfig_MinResource <Int32>
-Autotune_Mode <AutotuneMode>
-TrainingJobDefinition_OutputDataConfig <OutputDataConfig>
-WarmStartConfig_ParentHyperParameterTuningJob <ParentHyperParameterTuningJob[]>
-HyperParameterTuningJobConfig_RandomSeed <Int32>
-TrainingJobDefinition_ResourceConfig <ResourceConfig>
-TrainingJobDefinition_RoleArn <String>
-CheckpointConfig_S3Uri <String>
-VpcConfig_SecurityGroupId <String[]>
-TrainingJobDefinition_StaticHyperParameter <Hashtable>
-HyperParameterTuningJobConfig_Strategy <HyperParameterTuningJobStrategyType>
-VpcConfig_Subnet <String[]>
-Tag <Tag[]>
-TuningJobCompletionCriteria_TargetObjectiveMetricValue <Single>
-AlgorithmSpecification_TrainingImage <String>
-AlgorithmSpecification_TrainingInputMode <TrainingInputMode>
-TrainingJobDefinition <HyperParameterTrainingJobDefinition[]>
-HyperParameterTuningJobConfig_TrainingJobEarlyStoppingType <TrainingJobEarlyStoppingType>
-HyperParameterTuningJobObjective_Type <HyperParameterTuningJobObjectiveType>
-TuningObjective_Type <HyperParameterTuningJobObjectiveType>
-HyperParameterTuningResourceConfig_VolumeKmsKeyId <String>
-HyperParameterTuningResourceConfig_VolumeSizeInGB <Int32>
-WarmStartConfig_WarmStartType <HyperParameterTuningJobWarmStartType>
-Select <String>
-Force <SwitchParameter>
-ClientConfig <AmazonSageMakerConfig>

Description

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. A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields..

Parameters

-AlgorithmSpecification_AlgorithmName <String>
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_AlgorithmSpecification_AlgorithmName
-AlgorithmSpecification_MetricDefinition <MetricDefinition[]>
An array of MetricDefinition objects that specify the metrics that the algorithm emits. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_AlgorithmSpecification_MetricDefinitions
-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 Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_AlgorithmSpecification_TrainingImage
-AlgorithmSpecification_TrainingInputMode <TrainingInputMode>
The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_AlgorithmSpecification_TrainingInputMode
-Autotune_Mode <AutotuneMode>
Set Mode to Enabled if you want to use Autotune.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-BestObjectiveNotImproving_MaxNumberOfTrainingJobsNotImproving <Int32>
The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_TuningJobCompletionCriteria_BestObjectiveNotImproving_MaxNumberOfTrainingJobsNotImproving
-CheckpointConfig_LocalPath <String>
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_CheckpointConfig_LocalPath
-CheckpointConfig_S3Uri <String>
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_CheckpointConfig_S3Uri
-ClientConfig <AmazonSageMakerConfig>
Amazon.PowerShell.Cmdlets.SM.AmazonSageMakerClientCmdlet.ClientConfig
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-ConvergenceDetected_CompleteOnConvergence <CompleteOnConvergence>
A flag to stop a tuning job once AMT has detected that the job has converged.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_TuningJobCompletionCriteria_ConvergenceDetected_CompleteOnConvergence
-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?True (ByPropertyName)
-HyperbandStrategyConfig_MaxResource <Int32>
The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTraining attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters:
  • epochs
  • numepochs
  • n-epochs
  • n_epochs
  • num_epochs
If HyperbandStrategyConfig is unable to infer a value for MaxResource, it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions. For distributed training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_StrategyConfig_HyperbandStrategyConfig_MaxResource
-HyperbandStrategyConfig_MinResource <Int32>
The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_StrategyConfig_HyperbandStrategyConfig_MinResource
-HyperParameterRanges_AutoParameter <AutoParameter[]>
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_HyperParameterRanges_AutoParameters
-HyperParameterRanges_CategoricalParameterRange <CategoricalParameterRange[]>
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_HyperParameterRanges_CategoricalParameterRanges
-HyperParameterRanges_ContinuousParameterRange <ContinuousParameterRange[]>
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_HyperParameterRanges_ContinuousParameterRanges
-HyperParameterRanges_IntegerParameterRange <IntegerParameterRange[]>
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_HyperParameterRanges_IntegerParameterRanges
-HyperParameterTuningJobConfig_RandomSeed <Int32>
A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-HyperParameterTuningJobConfig_Strategy <HyperParameterTuningJobStrategyType>
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
-HyperParameterTuningJobConfig_TrainingJobEarlyStoppingType <TrainingJobEarlyStoppingType>
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband. This parameter can take on one of the following values (the default value is OFF):
OFF
Training jobs launched by the hyperparameter tuning job do not use early stopping.
AUTO
SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-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 Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
Required?True
Position?1
Accept pipeline input?True (ByValue, ByPropertyName)
-HyperParameterTuningJobObjective_MetricName <String>
The name of the metric to use for the objective metric.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_HyperParameterTuningJobObjective_MetricName
-HyperParameterTuningJobObjective_Type <HyperParameterTuningJobObjectiveType>
Whether to minimize or maximize the objective metric.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_HyperParameterTuningJobObjective_Type
-HyperParameterTuningResourceConfig_AllocationStrategy <HyperParameterTuningAllocationStrategy>
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_HyperParameterTuningResourceConfig_AllocationStrategy
-HyperParameterTuningResourceConfig_InstanceConfig <HyperParameterTuningInstanceConfig[]>
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs. Instead, use InstanceType, VolumeSizeInGB and InstanceCount. If you use InstanceConfigs, do not provide values for InstanceType, VolumeSizeInGB or InstanceCount. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_HyperParameterTuningResourceConfig_InstanceConfigs
-HyperParameterTuningResourceConfig_InstanceCount <Int32>
The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_HyperParameterTuningResourceConfig_InstanceCount
-HyperParameterTuningResourceConfig_InstanceType <TrainingInstanceType>
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_HyperParameterTuningResourceConfig_InstanceType
-HyperParameterTuningResourceConfig_VolumeKmsKeyId <String>
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.KMS Key ID:"1234abcd-12ab-34cd-56ef-1234567890ab"Amazon Resource Name (ARN) of a KMS key:"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_HyperParameterTuningResourceConfig_VolumeKmsKeyId
-HyperParameterTuningResourceConfig_VolumeSizeInGB <Int32>
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.SageMaker supports only the General Purpose SSD (gp2) storage volume type.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_HyperParameterTuningResourceConfig_VolumeSizeInGB
-ParameterRanges_AutoParameter <AutoParameter[]>
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_ParameterRanges_AutoParameters
-ParameterRanges_CategoricalParameterRange <CategoricalParameterRange[]>
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_ParameterRanges_CategoricalParameterRanges
-ParameterRanges_ContinuousParameterRange <ContinuousParameterRange[]>
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_ParameterRanges_ContinuousParameterRanges
-ParameterRanges_IntegerParameterRange <IntegerParameterRange[]>
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_ParameterRanges_IntegerParameterRanges
-ResourceLimits_MaxNumberOfTrainingJob <Int32>
The maximum number of training jobs that a hyperparameter tuning job can launch.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_ResourceLimits_MaxNumberOfTrainingJobs
-ResourceLimits_MaxParallelTrainingJob <Int32>
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_ResourceLimits_MaxParallelTrainingJobs
-ResourceLimits_MaxRuntimeInSecond <Int32>
The maximum time in seconds that a hyperparameter tuning job can run.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_ResourceLimits_MaxRuntimeInSeconds
-RetryStrategy_MaximumRetryAttempt <Int32>
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_RetryStrategy_MaximumRetryAttempts
-Select <String>
Use the -Select parameter to control the cmdlet output. The default value is 'HyperParameterTuningJobArn'. Specifying -Select '*' will result in the cmdlet returning the whole service response (Amazon.SageMaker.Model.CreateHyperParameterTuningJobResponse). Specifying the name of a property of type Amazon.SageMaker.Model.CreateHyperParameterTuningJobResponse will result in that property being returned. Specifying -Select '^ParameterName' will result in the cmdlet returning the selected cmdlet parameter value.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-StoppingCondition_MaxPendingTimeInSecond <Int32>
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.When working with training jobs that use capacity from training plans, not all Pending job states count against the MaxPendingTimeInSeconds limit. The following scenarios do not increment the MaxPendingTimeInSeconds counter:
  • The plan is in a Scheduled state: Jobs queued (in Pending status) before a plan's start date (waiting for scheduled start time)
  • Between capacity reservations: Jobs temporarily back to Pending status between two capacity reservation periods
MaxPendingTimeInSeconds only increments when jobs are actively waiting for capacity in an Active plan.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_StoppingCondition_MaxPendingTimeInSeconds
-StoppingCondition_MaxRuntimeInSecond <Int32>
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_StoppingCondition_MaxRuntimeInSeconds
-StoppingCondition_MaxWaitTimeInSecond <Int32>
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds. If the job does not complete during this time, SageMaker ends the job.When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_StoppingCondition_MaxWaitTimeInSeconds
-Tag <Tag[]>
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTags
-TrainingJobDefinition <HyperParameterTrainingJobDefinition[]>
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinitions
-TrainingJobDefinition_DefinitionName <String>
The job definition name.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-TrainingJobDefinition_EnableInterContainerTrafficEncryption <Boolean>
To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-TrainingJobDefinition_EnableManagedSpotTraining <Boolean>
A Boolean indicating whether managed spot training is enabled (True) or not (False).
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-TrainingJobDefinition_EnableNetworkIsolation <Boolean>
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-TrainingJobDefinition_Environment <Hashtable>
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-TrainingJobDefinition_InputDataConfig <Channel[]>
An array of Channel objects that specify the input for the training jobs that the tuning job launches. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-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?True (ByPropertyName)
-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 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.If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-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?True (ByPropertyName)
-TrainingJobDefinition_StaticHyperParameter <Hashtable>
Specifies the values of hyperparameters that do not change for the tuning job. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_StaticHyperParameters
-TuningJobCompletionCriteria_TargetObjectiveMetricValue <Single>
The value of the objective metric.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHyperParameterTuningJobConfig_TuningJobCompletionCriteria_TargetObjectiveMetricValue
-TuningObjective_MetricName <String>
The name of the metric to use for the objective metric.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_TuningObjective_MetricName
-TuningObjective_Type <HyperParameterTuningJobObjectiveType>
Whether to minimize or maximize the objective metric.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_TuningObjective_Type
-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. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_VpcConfig_SecurityGroupIds
-VpcConfig_Subnet <String[]>
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingJobDefinition_VpcConfig_Subnets
-WarmStartConfig_ParentHyperParameterTuningJob <ParentHyperParameterTuningJob[]>
An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs. Starting with version 4 of the SDK this property will default to null. If no data for this property is returned from the service the property will also be null. This was changed to improve performance and allow the SDK and caller to distinguish between a property not set or a property being empty to clear out a value. To retain the previous SDK behavior set the AWSConfigs.InitializeCollections static property to true.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesWarmStartConfig_ParentHyperParameterTuningJobs
-WarmStartConfig_WarmStartType <HyperParameterTuningJobWarmStartType>
Specifies one of the following:
IDENTICAL_DATA_AND_ALGORITHM
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
TRANSFER_LEARNING
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)

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?True (ByPropertyName)
AliasesAK
-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?True (ByValue, ByPropertyName)
-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?True (ByPropertyName)
-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?True (ByValue, ByPropertyName)
-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.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?True (ByPropertyName)
AliasesAWSProfilesLocation, ProfilesLocation
-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?True (ByPropertyName)
AliasesStoredCredentials, AWSProfileName
-Region <Object>
The system name of an AWS region or an AWSRegion instance. This governs the endpoint that will be used when calling service operations. Note that the AWS resources referenced in a call are usually region-specific.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesRegionToCall
-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?True (ByPropertyName)
AliasesSK, SecretAccessKey
-SessionToken <String>
The session token if the access and secret keys are temporary session-based credentials.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesST

Outputs

System.String or Amazon.SageMaker.Model.CreateHyperParameterTuningJobResponse
This cmdlet returns a System.String object. The service call response (type Amazon.SageMaker.Model.CreateHyperParameterTuningJobResponse) can be returned by specifying '-Select *'.
Developer Guide
Service API Reference
AWS Tools for PowerShell User Guide

Supported Version

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