CreateInferenceExperiment

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.

Type: InferenceExperimentDataStorageConfig object

Required: No

A description for the inference experiment.

Type: String

Length Constraints: Minimum length of 0. Maximum length of 1024.

Pattern: .*

Required: No

The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.

Type: String

Length Constraints: Minimum length of 0. Maximum length of 63.

Pattern: [a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}

Required: Yes

The AWS Key Management Service (AWS 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:

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 AWS KMS in the AWS Key Management Service Developer Guide.

Type: String

Length Constraints: Minimum length of 0. Maximum length of 2048.

Pattern: [a-zA-Z0-9:/_-]*

Required: No

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.

Type: Array of ModelVariantConfig objects

Array Members: Minimum number of 1 item. Maximum number of 2 items.

Required: Yes

The name for the inference experiment.

Type: String

Length Constraints: Minimum length of 1. Maximum length of 120.

Pattern: [a-zA-Z0-9](-*[a-zA-Z0-9]){0,119}

Required: Yes

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.

Type: String

Length Constraints: Minimum length of 20. Maximum length of 2048.

Pattern: arn:aws[a-z\-]*:iam::\d{12}:role/?[a-zA-Z_0-9+=,.@\-_/]+

Required: Yes

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.

Type: InferenceExperimentSchedule object

Required: No

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.

Type: ShadowModeConfig object

Required: Yes

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 Tagging your AWS Resources.

Type: Array of Tag objects

Array Members: Minimum number of 0 items. Maximum number of 50 items.

Required: No

The type of the inference experiment that you want to run. The following types of experiments are possible:

Type: String

Valid Values: ShadowMode

Required: Yes

The ARN for your inference experiment.

Type: String

Length Constraints: Minimum length of 0. Maximum length of 256.

Pattern: arn:aws[a-z\-]*:sagemaker:[a-z0-9\-]*:[0-9]{12}:inference-experiment/.*

Resource being accessed is in use.

HTTP Status Code: 400

You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

HTTP Status Code: 400