AWS Tools for Windows PowerShell
Command Reference

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Synopsis

Calls the Amazon SageMaker Service CreateAutoMLJob API operation.

Syntax

New-SMAutoMLJob
-AutoMLJobName <String>
-ModelDeployConfig_AutoGenerateEndpointName <Boolean>
-SecurityConfig_EnableInterContainerTrafficEncryption <Boolean>
-ModelDeployConfig_EndpointName <String>
-CandidateGenerationConfig_FeatureSpecificationS3Uri <String>
-GenerateCandidateDefinitionsOnly <Boolean>
-InputDataConfig <AutoMLChannel[]>
-OutputDataConfig_KmsKeyId <String>
-CompletionCriteria_MaxAutoMLJobRuntimeInSecond <Int32>
-CompletionCriteria_MaxCandidate <Int32>
-CompletionCriteria_MaxRuntimePerTrainingJobInSecond <Int32>
-AutoMLJobObjective_MetricName <AutoMLMetricEnum>
-ProblemType <ProblemType>
-RoleArn <String>
-OutputDataConfig_S3OutputPath <String>
-VpcConfig_SecurityGroupId <String[]>
-VpcConfig_Subnet <String[]>
-Tag <Tag[]>
-DataSplitConfig_ValidationFraction <Single>
-SecurityConfig_VolumeKmsKeyId <String>
-Select <String>
-PassThru <SwitchParameter>
-Force <SwitchParameter>

Description

Creates an Autopilot job. Find the best-performing model after you run an Autopilot job by calling . For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.

Parameters

-AutoMLJobName <String>
Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.
Required?True
Position?1
Accept pipeline input?True (ByValue, ByPropertyName)
-AutoMLJobObjective_MetricName <AutoMLMetricEnum>
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.Here are the options:
  • MSE: The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive: the better a model is at predicting the actual values, the smaller the MSE value is. When the data contains outliers, they tend to dominate the MSE, which might cause subpar prediction performance.
  • Accuracy: The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for binary and multiclass classification. It measures how close the predicted class values are to the actual values. Accuracy values vary between zero and one: one indicates perfect accuracy and zero indicates perfect inaccuracy.
  • F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class and false when they do not. Precision is the ratio of the true positive predictions to all positive predictions (including the false positives) in a data set and measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances and measures how completely a model predicts the actual class members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary between zero and one: one indicates the best possible performance and zero the worst.
  • AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities into classifications. The relevant curve is the receiver operating characteristic curve that plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so provides an aggregated measure of the model performance across all possible classification thresholds. The AUC score can also be interpreted as the probability that a randomly selected positive data point is more likely to be predicted positive than a randomly selected negative example. AUC scores vary between zero and one: a score of one indicates perfect accuracy and a score of one half indicates that the prediction is not better than a random classifier. Values under one half predict less accurately than a random predictor. But such consistently bad predictors can simply be inverted to obtain better than random predictors.
  • F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you have multiple classes to predict. You just calculate the precision and recall for each class as you did for the positive class in binary classification. Then, use these values to calculate the F1 score for each class and average them to obtain the F1macro score. F1macro scores vary between zero and one: one indicates the best possible performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
  • MSE: for regression.
  • F1: for binary classification
  • Accuracy: for multiclass classification.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-CandidateGenerationConfig_FeatureSpecificationS3Uri <String>
A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job (optional). This file should be in json format as shown below: { "FeatureAttributeNames":["col1", "col2", ...] }.The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] is case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAutoMLJobConfig_CandidateGenerationConfig_FeatureSpecificationS3Uri
-CompletionCriteria_MaxAutoMLJobRuntimeInSecond <Int32>
The maximum runtime, in seconds, an AutoML job has to complete.If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, will not be completed.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAutoMLJobConfig_CompletionCriteria_MaxAutoMLJobRuntimeInSeconds
-CompletionCriteria_MaxCandidate <Int32>
The maximum number of times a training job is allowed to run.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAutoMLJobConfig_CompletionCriteria_MaxCandidates
-CompletionCriteria_MaxRuntimePerTrainingJobInSecond <Int32>
The maximum time, in seconds, that each training job is allowed to run as part of a hyperparameter tuning job. For more information, see the used by the action.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAutoMLJobConfig_CompletionCriteria_MaxRuntimePerTrainingJobInSeconds
-DataSplitConfig_ValidationFraction <Single>
The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAutoMLJobConfig_DataSplitConfig_ValidationFraction
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)
-GenerateCandidateDefinitionsOnly <Boolean>
Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-InputDataConfig <AutoMLChannel[]>
An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
-ModelDeployConfig_AutoGenerateEndpointName <Boolean>
Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False.If you set AutoGenerateEndpointName to True, do not specify the EndpointName; otherwise a 400 error is thrown.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-ModelDeployConfig_EndpointName <String>
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.Specify the EndpointName if and only if you set AutoGenerateEndpointName to False; otherwise a 400 error is thrown.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-OutputDataConfig_KmsKeyId <String>
The Amazon Web Services KMS encryption key ID.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-OutputDataConfig_S3OutputPath <String>
The Amazon S3 output path. Must be 128 characters or less.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
-PassThru <SwitchParameter>
Changes the cmdlet behavior to return the value passed to the AutoMLJobName parameter. The -PassThru parameter is deprecated, use -Select '^AutoMLJobName' instead. This parameter will be removed in a future version.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-ProblemType <ProblemType>
Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-RoleArn <String>
The ARN of the role that is used to access the data.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
-SecurityConfig_EnableInterContainerTrafficEncryption <Boolean>
Whether to use traffic encryption between the container layers.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAutoMLJobConfig_SecurityConfig_EnableInterContainerTrafficEncryption
-SecurityConfig_VolumeKmsKeyId <String>
The key used to encrypt stored data.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAutoMLJobConfig_SecurityConfig_VolumeKmsKeyId
-Select <String>
Use the -Select parameter to control the cmdlet output. The default value is 'AutoMLJobArn'. Specifying -Select '*' will result in the cmdlet returning the whole service response (Amazon.SageMaker.Model.CreateAutoMLJobResponse). Specifying the name of a property of type Amazon.SageMaker.Model.CreateAutoMLJobResponse 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)
-Tag <Tag[]>
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTags
-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?True (ByPropertyName)
AliasesAutoMLJobConfig_SecurityConfig_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.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAutoMLJobConfig_SecurityConfig_VpcConfig_Subnets

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

This cmdlet returns a System.String object. The service call response (type Amazon.SageMaker.Model.CreateAutoMLJobResponse) 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