Creates an inference experiment using the configurations specified in the request.
See https://www.paws-r-sdk.com/docs/sagemaker_create_inference_experiment/ for full documentation.
sagemaker_create_inference_experiment(
Name,
Type,
Schedule = NULL,
Description = NULL,
RoleArn,
EndpointName,
ModelVariants,
DataStorageConfig = NULL,
ShadowModeConfig,
KmsKey = NULL,
Tags = NULL
)[required] The name for the inference experiment.
[required] 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.
The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.
A description for the inference experiment.
[required] 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] The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
[required] 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.
The Amazon S3 location and configuration for storing inference request and response data.
This is an optional parameter that you can use for data capture. For more information, see Capture data.
[required] The configuration of ShadowMode inference experiment type. Use this
field to specify a production variant which takes all the inference
requests, and a shadow variant to which Amazon SageMaker replicates a
percentage of the inference requests. For the shadow variant also
specify the percentage of requests that Amazon SageMaker replicates.
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 create_endpoint and
update_endpoint requests. For more
information, see Using Key Policies in Amazon Web Services KMS
in the Amazon Web Services Key Management Service Developer Guide.
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.