Struct InvokeEndpointFluentBuilder 

pub struct InvokeEndpointFluentBuilder { /* private fields */ }

Fluent builder constructing a request to InvokeEndpoint.

After you deploy a model into production using Amazon SageMaker AI hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint.

For an overview of Amazon SageMaker AI, see How It Works.

Amazon SageMaker AI strips all POST headers except those supported by the API. Amazon SageMaker AI might add additional headers. You should not rely on the behavior of headers outside those enumerated in the request syntax.

Calls to InvokeEndpoint are authenticated by using Amazon Web Services Signature Version 4. For information, see Authenticating Requests (Amazon Web Services Signature Version 4) in the Amazon S3 API Reference.

A customer's model containers must respond to requests within 60 seconds. The model itself can have a maximum processing time of 60 seconds before responding to invocations. If your model is going to take 50-60 seconds of processing time, the SDK socket timeout should be set to be 70 seconds.

Endpoints are scoped to an individual account, and are not public. The URL does not contain the account ID, but Amazon SageMaker AI determines the account ID from the authentication token that is supplied by the caller.

Implementations

impl InvokeEndpointFluentBuilder

pub fn as_input(&self) -> &InvokeEndpointInputBuilder

Access the InvokeEndpoint as a reference.

pub async fn send( self, ) -> Result<InvokeEndpointOutput, SdkError<InvokeEndpointError, HttpResponse>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn customize( self, ) -> CustomizableOperation<InvokeEndpointOutput, InvokeEndpointError, Self>

Consumes this builder, creating a customizable operation that can be modified before being sent.

pub fn endpoint_name(self, input: impl Into<String>) -> Self

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

pub fn set_endpoint_name(self, input: Option<String>) -> Self

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

pub fn get_endpoint_name(&self) -> &Option<String>

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

pub fn body(self, input: Blob) -> Self

Provides input data, in the format specified in the ContentType request header. Amazon SageMaker AI 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.

pub fn set_body(self, input: Option<Blob>) -> Self

Provides input data, in the format specified in the ContentType request header. Amazon SageMaker AI 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.

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

Provides input data, in the format specified in the ContentType request header. Amazon SageMaker AI 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.

pub fn content_type(self, input: impl Into<String>) -> Self

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

pub fn set_content_type(self, input: Option<String>) -> Self

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

pub fn get_content_type(&self) -> &Option<String>

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

pub fn accept(self, input: impl Into<String>) -> Self

The desired MIME type of the inference response from the model container.

pub fn set_accept(self, input: Option<String>) -> Self

The desired MIME type of the inference response from the model container.

pub fn get_accept(&self) -> &Option<String>

The desired MIME type of the inference response from the model container.

pub fn custom_attributes(self, input: impl Into<String>) -> Self

Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker AI 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 AI Python SDK.

pub fn set_custom_attributes(self, input: Option<String>) -> Self

Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker AI 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 AI Python SDK.

pub fn get_custom_attributes(&self) -> &Option<String>

Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker AI 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 AI Python SDK.

pub fn target_model(self, input: impl Into<String>) -> Self

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

pub fn set_target_model(self, input: Option<String>) -> Self

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

pub fn get_target_model(&self) -> &Option<String>

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

pub fn target_variant(self, input: impl Into<String>) -> Self

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

pub fn set_target_variant(self, input: Option<String>) -> Self

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

pub fn get_target_variant(&self) -> &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

pub fn target_container_hostname(self, input: impl Into<String>) -> Self

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

pub fn set_target_container_hostname(self, input: Option<String>) -> Self

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

pub fn get_target_container_hostname(&self) -> &Option<String>

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

pub fn inference_id(self, input: impl Into<String>) -> Self

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.

pub fn set_inference_id(self, input: Option<String>) -> Self

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.

pub fn get_inference_id(&self) -> &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.

pub fn enable_explanations(self, input: impl Into<String>) -> Self

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.

pub fn set_enable_explanations(self, input: Option<String>) -> Self

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.

pub fn get_enable_explanations(&self) -> &Option<String>

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.

pub fn inference_component_name(self, input: impl Into<String>) -> Self

If the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke.

pub fn set_inference_component_name(self, input: Option<String>) -> Self

If the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke.

pub fn get_inference_component_name(&self) -> &Option<String>

If the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke.

pub fn session_id(self, input: impl Into<String>) -> Self

Creates a stateful session or identifies an existing one. You can do one of the following:

• Create a stateful session by specifying the value NEW_SESSION.

• Send your request to an existing stateful session by specifying the ID of that session.

With a stateful session, you can send multiple requests to a stateful model. When you create a session with a stateful model, the model must create the session ID and set the expiration time. The model must also provide that information in the response to your request. You can get the ID and timestamp from the NewSessionId response parameter. For any subsequent request where you specify that session ID, SageMaker AI routes the request to the same instance that supports the session.

pub fn set_session_id(self, input: Option<String>) -> Self

Creates a stateful session or identifies an existing one. You can do one of the following:

• Create a stateful session by specifying the value NEW_SESSION.

• Send your request to an existing stateful session by specifying the ID of that session.

With a stateful session, you can send multiple requests to a stateful model. When you create a session with a stateful model, the model must create the session ID and set the expiration time. The model must also provide that information in the response to your request. You can get the ID and timestamp from the NewSessionId response parameter. For any subsequent request where you specify that session ID, SageMaker AI routes the request to the same instance that supports the session.

pub fn get_session_id(&self) -> &Option<String>

Creates a stateful session or identifies an existing one. You can do one of the following:

• Create a stateful session by specifying the value NEW_SESSION.

• Send your request to an existing stateful session by specifying the ID of that session.

With a stateful session, you can send multiple requests to a stateful model. When you create a session with a stateful model, the model must create the session ID and set the expiration time. The model must also provide that information in the response to your request. You can get the ID and timestamp from the NewSessionId response parameter. For any subsequent request where you specify that session ID, SageMaker AI routes the request to the same instance that supports the session.

Trait Implementations

impl Clone for InvokeEndpointFluentBuilder

fn clone(&self) -> InvokeEndpointFluentBuilder

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for InvokeEndpointFluentBuilder

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

Formats the value using the given formatter.