sagemakerruntime_invoke_endpoint_async: After you deploy a model into production using Amazon...

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

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

Description

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

See https://www.paws-r-sdk.com/docs/sagemakerruntime_invoke_endpoint_async/ for full documentation.

Usage

sagemakerruntime_invoke_endpoint_async(
  EndpointName,
  ContentType = NULL,
  Accept = NULL,
  CustomAttributes = NULL,
  InferenceId = NULL,
  InputLocation,
  RequestTTLSeconds = NULL,
  InvocationTimeoutSeconds = NULL
)

Arguments

EndpointName	[required] The name of the endpoint that you specified when you created the endpoint using the CreateEndpoint API.
ContentType	The MIME type of the input data in the request body.
Accept	The desired MIME type of the inference response from the model container.
CustomAttributes	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.
InferenceId	The identifier for the inference request. Amazon SageMaker will generate an identifier for you if none is specified.
InputLocation	[required] The Amazon S3 URI where the inference request payload is stored.
RequestTTLSeconds	Maximum age in seconds a request can be in the queue before it is marked as expired. The default is 6 hours, or 21,600 seconds.
InvocationTimeoutSeconds	Maximum amount of time in seconds a request can be processed before it is marked as expired. The default is 15 minutes, or 900 seconds.

paws.machine.learning documentation built on Sept. 12, 2024, 6:23 a.m.