Interface InvokeEndpointRequest.Builder
- All Superinterfaces:
- AwsRequest.Builder,- Buildable,- CopyableBuilder<InvokeEndpointRequest.Builder,,- InvokeEndpointRequest> - SageMakerRuntimeRequest.Builder,- SdkBuilder<InvokeEndpointRequest.Builder,,- InvokeEndpointRequest> - SdkPojo,- SdkRequest.Builder
- Enclosing class:
- InvokeEndpointRequest
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Method SummaryModifier and TypeMethodDescriptionThe desired MIME type of the inference response from the model container.Provides input data, in the format specified in theContentTyperequest header.contentType(String contentType) The MIME type of the input data in the request body.customAttributes(String customAttributes) Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker AI endpoint.enableExplanations(String enableExplanations) An optional JMESPath expression used to override theEnableExplanationsparameter of theClarifyExplainerConfigAPI.endpointName(String endpointName) The name of the endpoint that you specified when you created the endpoint using the CreateEndpoint API.inferenceComponentName(String inferenceComponentName) If the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke.inferenceId(String inferenceId) If you provide a value, it is added to the captured data when you enable data capture on the endpoint.overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer) Add an optional request override configuration.overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration) Add an optional request override configuration.Creates a stateful session or identifies an existing one.targetContainerHostname(String targetContainerHostname) If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke.targetModel(String targetModel) The model to request for inference when invoking a multi-model endpoint.targetVariant(String targetVariant) Specify the production variant to send the inference request to when invoking an endpoint that is running two or more variants.Methods inherited from interface software.amazon.awssdk.awscore.AwsRequest.BuilderoverrideConfigurationMethods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuildercopyMethods inherited from interface software.amazon.awssdk.services.sagemakerruntime.model.SageMakerRuntimeRequest.BuilderbuildMethods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilderapplyMutation, buildMethods inherited from interface software.amazon.awssdk.core.SdkPojoequalsBySdkFields, sdkFieldNameToField, sdkFields
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Method Details- 
endpointNameThe name of the endpoint that you specified when you created the endpoint using the CreateEndpoint API. - Parameters:
- endpointName- The name of the endpoint that you specified when you created the endpoint using the CreateEndpoint API.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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bodyProvides input data, in the format specified in the ContentTyperequest 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. - Parameters:
- body- Provides input data, in the format specified in the- ContentTyperequest 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. 
- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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contentTypeThe MIME type of the input data in the request body. - Parameters:
- contentType- The MIME type of the input data in the request body.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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acceptThe desired MIME type of the inference response from the model container. - Parameters:
- accept- The desired MIME type of the inference response from the model container.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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customAttributesProvides 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. - Parameters:
- customAttributes- 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. 
- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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targetModelThe model to request for inference when invoking a multi-model endpoint. - Parameters:
- targetModel- The model to request for inference when invoking a multi-model endpoint.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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targetVariantSpecify 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 - Parameters:
- targetVariant- 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 
- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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targetContainerHostnameIf the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke. - Parameters:
- targetContainerHostname- If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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inferenceIdIf 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. - Parameters:
- inferenceId- 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.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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enableExplanationsAn optional JMESPath expression used to override the EnableExplanationsparameter of theClarifyExplainerConfigAPI. See the EnableExplanations section in the developer guide for more information.- Parameters:
- enableExplanations- An optional JMESPath expression used to override the- EnableExplanationsparameter of the- ClarifyExplainerConfigAPI. See the EnableExplanations section in the developer guide for more information.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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inferenceComponentNameIf the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke. - Parameters:
- inferenceComponentName- If the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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sessionIdCreates 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.
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 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 NewSessionIdresponse parameter. For any subsequent request where you specify that session ID, SageMaker AI routes the request to the same instance that supports the session.- Parameters:
- sessionId- 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.
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        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 - NewSessionIdresponse parameter. For any subsequent request where you specify that session ID, SageMaker AI routes the request to the same instance that supports the session.
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- Returns:
- Returns a reference to this object so that method calls can be chained together.
 
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overrideConfigurationInvokeEndpointRequest.Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration) Description copied from interface:AwsRequest.BuilderAdd an optional request override configuration.- Specified by:
- overrideConfigurationin interface- AwsRequest.Builder
- Parameters:
- overrideConfiguration- The override configuration.
- Returns:
- This object for method chaining.
 
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overrideConfigurationInvokeEndpointRequest.Builder overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer) Description copied from interface:AwsRequest.BuilderAdd an optional request override configuration.- Specified by:
- overrideConfigurationin interface- AwsRequest.Builder
- Parameters:
- builderConsumer- A- Consumerto which an empty- AwsRequestOverrideConfiguration.Builderwill be given.
- Returns:
- This object for method chaining.
 
 
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