class Aws::SageMakerRuntime::Types::InvokeEndpointInput
@note When making an API call, you may pass InvokeEndpointInput
data as a hash: { endpoint_name: "EndpointName", # required body: "data", # required content_type: "Header", accept: "Header", custom_attributes: "CustomAttributesHeader", target_model: "TargetModelHeader", target_variant: "TargetVariantHeader", target_container_hostname: "TargetContainerHostnameHeader", inference_id: "InferenceId", }
@!attribute [rw] endpoint_name
The name of the endpoint that you specified when you created the endpoint using the [CreateEndpoint][1] API. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpoint.html @return [String]
@!attribute [rw] body
Provides input data, in the format specified in the `ContentType` request header. Amazon SageMaker passes all of the data in the body to the model. For information about the format of the request body, see [Common Data Formats-Inference][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html @return [String]
@!attribute [rw] content_type
The MIME type of the input data in the request body. @return [String]
@!attribute [rw] accept
The desired MIME type of the inference in the response. @return [String]
@!attribute [rw] custom_attributes
Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is forwarded verbatim. You could use this value, for example, to provide an ID that you can use to track a request or to provide other metadata that a service endpoint was programmed to process. The value must consist of no more than 1024 visible US-ASCII characters as specified in [Section 3.3.6. Field Value Components][1] of the Hypertext Transfer Protocol (HTTP/1.1). The code in your model is responsible for setting or updating any custom attributes in the response. If your code does not set this value in the response, an empty value is returned. For example, if a custom attribute represents the trace ID, your model can prepend the custom attribute with `Trace ID:` in your post-processing function. This feature is currently supported in the AWS SDKs but not in the Amazon SageMaker Python SDK. [1]: https://tools.ietf.org/html/rfc7230#section-3.2.6 @return [String]
@!attribute [rw] target_model
The model to request for inference when invoking a multi-model endpoint. @return [String]
@!attribute [rw] target_variant
Specify the production variant to send the inference request to when invoking an endpoint that is running two or more variants. Note that this parameter overrides the default behavior for the endpoint, which is to distribute the invocation traffic based on the variant weights. For information about how to use variant targeting to perform a/b testing, see [Test models in production][1] [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html @return [String]
@!attribute [rw] target_container_hostname
If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke. @return [String]
@!attribute [rw] inference_id
If you provide a value, it is added to the captured data when you enable data capture on the endpoint. For information about data capture, see [Capture Data][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture.html @return [String]
@see docs.aws.amazon.com/goto/WebAPI/runtime.sagemaker-2017-05-13/InvokeEndpointInput AWS API Documentation
Constants
- SENSITIVE