Amazon.PowerShell.Cmdlets.SM.AmazonSageMakerClientCmdlet.ClientConfig
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
The list of all content type headers that Amazon SageMaker AI will treat as CSV and capture accordingly. 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) |
Aliases | DataStorageConfig_ContentType_CsvContentTypes |
The list of all content type headers that SageMaker AI will treat as JSON and capture accordingly. 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) |
Aliases | DataStorageConfig_ContentType_JsonContentTypes |
-DataStorageConfig_Destination <
String>
The Amazon S3 bucket where the inference request and response data is stored.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
-DataStorageConfig_KmsKey <
String>
The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
A description for the inference experiment.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
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) |
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The
KmsKey can be any of the following formats:
- 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"
- KMS key Alias"alias/ExampleAlias"
- Amazon Resource Name (ARN) of a KMS key Alias"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call
kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for
OutputDataConfig. If you use a bucket policy with an
s3:PutObject permission that only allows objects with server-side encryption, set the condition key of
s3:x-amz-server-side-encryption to
"aws:kms". For more information, see
KMS managed Encryption Keys in the
Amazon Simple Storage Service Developer Guide. The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint and
UpdateEndpoint requests. For more information, see
Using Key Policies in Amazon Web Services KMS in the
Amazon Web Services Key Management Service Developer Guide.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
An array of ModelVariantConfig objects. There is one for each variant in the inference experiment. Each ModelVariantConfig object in the array describes the infrastructure configuration for the corresponding variant. 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? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | ModelVariants |
The name for the inference experiment.
Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
-Schedule_EndTime <DateTime>
The timestamp at which the inference experiment ended or will end.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
-Schedule_StartTime <DateTime>
The timestamp at which the inference experiment started or will start.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Use the -Select parameter to control the cmdlet output. The default value is 'InferenceExperimentArn'. Specifying -Select '*' will result in the cmdlet returning the whole service response (Amazon.SageMaker.Model.CreateInferenceExperimentResponse). Specifying the name of a property of type Amazon.SageMaker.Model.CreateInferenceExperimentResponse 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) |
List of shadow variant configurations. 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? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | ShadowModeConfig_ShadowModelVariants |
-ShadowModeConfig_SourceModelVariantName <
String>
The name of the production variant, which takes all the inference requests.
Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
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 your Amazon Web Services Resources. 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) |
Aliases | Tags |
The type of the inference experiment that you want to run. The following types of experiments are possible:
- ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.
Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |