Struct aws_sdk_sagemakerruntime::input::InvokeEndpointInput

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#[non_exhaustive]
pub struct InvokeEndpointInput { /* private fields */ }

Implementations

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impl InvokeEndpointInput

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pub async fn make_operation(
self,
_config: &Config
) -> Result<Operation<InvokeEndpoint, AwsResponseRetryClassifier>, BuildError>

Consumes the builder and constructs an Operation<InvokeEndpoint>

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pub fn builder() -> Builder

Creates a new builder-style object to manufacture InvokeEndpointInput.

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impl InvokeEndpointInput

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pub fn endpoint_name(&self) -> Option<&str>

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

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pub fn body(&self) -> Option<&Blob>

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.

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pub fn content_type(&self) -> Option<&str>

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

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pub fn accept(&self) -> Option<&str>

The desired MIME type of the inference in the response.

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pub fn custom_attributes(&self) -> Option<&str>

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 Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK.

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pub fn target_model(&self) -> Option<&str>

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

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pub fn target_variant(&self) -> Option<&str>

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

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pub fn target_container_hostname(&self) -> Option<&str>

If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke.

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pub fn inference_id(&self) -> Option<&str>

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.

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pub fn enable_explanations(&self) -> Option<&str>

An optional JMESPath expression used to override the EnableExplanations parameter of the ClarifyExplainerConfig API. See the EnableExplanations section in the developer guide for more information.