Struct rusoto_sagemaker_runtime::InvokeEndpointInput

pub struct InvokeEndpointInput {
    pub accept: Option<String>,
    pub body: Bytes,
    pub content_type: Option<String>,
    pub custom_attributes: Option<String>,
    pub endpoint_name: String,
    pub inference_id: Option<String>,
    pub target_model: Option<String>,
    pub target_variant: Option<String>,
}

Fields

accept: Option<String>

The desired MIME type of the inference in the response.

body: Bytes

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.

content_type: Option<String>

The MIME type of the input data in the request body.

custom_attributes: Option<String>

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 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.

endpoint_name: String

The name of the endpoint that you specified when you created the endpoint using the CreateEndpoint API.

inference_id: Option<String>

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.

target_model: Option<String>

The model to request for inference when invoking a multi-model endpoint.

target_variant: Option<String>

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

Trait Implementations

impl Clone for InvokeEndpointInput

pub fn clone(&self) -> InvokeEndpointInput

Returns a copy of the value.

pub fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for InvokeEndpointInput

pub fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for InvokeEndpointInput

pub fn default() -> InvokeEndpointInput

Returns the "default value" for a type.

impl PartialEq<InvokeEndpointInput> for InvokeEndpointInput

pub fn eq(&self, other: &InvokeEndpointInput) -> bool

This method tests for self and other values to be equal, and is used by ==.

pub fn ne(&self, other: &InvokeEndpointInput) -> bool

This method tests for !=.

impl Serialize for InvokeEndpointInput

pub fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for InvokeEndpointInput