2024/09/09 - Amazon SageMaker Runtime - 2 updated api methods
Changes Amazon Sagemaker supports orchestrating SageMaker HyperPod clusters with Amazon EKS
{'SessionId': 'string'}
Response {'ClosedSessionId': 'string', 'NewSessionId': 'string'}
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.
For an overview of Amazon SageMaker, see How It Works.
Amazon SageMaker strips all POST headers except those supported by the API. Amazon SageMaker 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.
See also: AWS API Documentation
Request Syntax
client.invoke_endpoint(
EndpointName='string',
Body=b'bytes'|file,
ContentType='string',
Accept='string',
CustomAttributes='string',
TargetModel='string',
TargetVariant='string',
TargetContainerHostname='string',
InferenceId='string',
EnableExplanations='string',
InferenceComponentName='string',
SessionId='string'
)
string
[REQUIRED]
The name of the endpoint that you specified when you created the endpoint using the CreateEndpoint API.
bytes or seekable file-like object
[REQUIRED]
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.
string
The MIME type of the input data in the request body.
string
The desired MIME type of the inference response from the model container.
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 Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK.
string
The model to request for inference when invoking a multi-model endpoint.
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
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.
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.
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.
string
If the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke.
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 routes the request to the same instance that supports the session.
dict
Response Syntax
{
'Body': b'bytes'|file,
'ContentType': 'string',
'InvokedProductionVariant': 'string',
'CustomAttributes': 'string',
'NewSessionId': 'string',
'ClosedSessionId': 'string'
}
Response Structure
(dict) --
Body (bytes or seekable file-like object) --
Includes the inference provided by the model.
For information about the format of the response body, see Common Data Formats-Inference.
If the explainer is activated, the body includes the explanations provided by the model. For more information, see the Response section under Invoke the Endpoint in the Developer Guide.
ContentType (string) --
The MIME type of the inference returned from the model container.
InvokedProductionVariant (string) --
Identifies the production variant that was invoked.
CustomAttributes (string) --
Provides additional information in the response about the inference returned by 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 return an ID received in the CustomAttributes header of a request or other metadata that a service endpoint was programmed to produce. 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). If the customer wants the custom attribute returned, the model must set the custom attribute to be included on the way back.
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.
NewSessionId (string) --
If you created a stateful session with your request, the ID and expiration time that the model assigns to that session.
ClosedSessionId (string) --
If you closed a stateful session with your request, the ID of that session.
{'SessionId': 'string'}
Invokes a model at the specified endpoint to return the inference response as a stream. The inference stream provides the response payload incrementally as a series of parts. Before you can get an inference stream, you must have access to a model that's deployed using Amazon SageMaker hosting services, and the container for that model must support inference streaming.
For more information that can help you use this API, see the following sections in the Amazon SageMaker Developer Guide:
For information about how to add streaming support to a model, see How Containers Serve Requests.
For information about how to process the streaming response, see Invoke real-time endpoints.
Before you can use this operation, your IAM permissions must allow the sagemaker:InvokeEndpoint action. For more information about Amazon SageMaker actions for IAM policies, see Actions, resources, and condition keys for Amazon SageMaker in the IAM Service Authorization Reference.
Amazon SageMaker strips all POST headers except those supported by the API. Amazon SageMaker might add additional headers. You should not rely on the behavior of headers outside those enumerated in the request syntax.
Calls to InvokeEndpointWithResponseStream 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.
See also: AWS API Documentation
Request Syntax
client.invoke_endpoint_with_response_stream(
EndpointName='string',
Body=b'bytes'|file,
ContentType='string',
Accept='string',
CustomAttributes='string',
TargetVariant='string',
TargetContainerHostname='string',
InferenceId='string',
InferenceComponentName='string',
SessionId='string'
)
string
[REQUIRED]
The name of the endpoint that you specified when you created the endpoint using the CreateEndpoint API.
bytes or seekable file-like object
[REQUIRED]
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.
string
The MIME type of the input data in the request body.
string
The desired MIME type of the inference response from the model container.
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 Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK.
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
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.
string
An identifier that you assign to your request.
string
If the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke for a streaming response.
string
The ID of a stateful session to handle your request.
You can't create a stateful session by using the InvokeEndpointWithResponseStream action. Instead, you can create one by using the InvokeEndpoint action. In your request, you specify NEW_SESSION for the SessionId request parameter. The response to that request provides the session ID for the NewSessionId response parameter.
dict
The response of this operation contains an :class:`.EventStream` member. When iterated the :class:`.EventStream` will yield events based on the structure below, where only one of the top level keys will be present for any given event.
Response Syntax
{
'Body': EventStream({
'PayloadPart': {
'Bytes': b'bytes'
},
'ModelStreamError': {
'Message': 'string',
'ErrorCode': 'string'
},
'InternalStreamFailure': {
'Message': 'string'
}
}),
'ContentType': 'string',
'InvokedProductionVariant': 'string',
'CustomAttributes': 'string'
}
Response Structure
(dict) --
Body (:class:`.EventStream`) --
A stream of payload parts. Each part contains a portion of the response for a streaming inference request.
PayloadPart (dict) --
A wrapper for pieces of the payload that's returned in response to a streaming inference request. A streaming inference response consists of one or more payload parts.
Bytes (bytes) --
A blob that contains part of the response for your streaming inference request.
ModelStreamError (dict) --
An error occurred while streaming the response body. This error can have the following error codes:
ModelInvocationTimeExceeded
The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker.
StreamBroken
The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.
Message (string) --
ErrorCode (string) --
This error can have the following error codes:
ModelInvocationTimeExceeded
The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker.
StreamBroken
The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.
InternalStreamFailure (dict) --
The stream processing failed because of an unknown error, exception or failure. Try your request again.
Message (string) --
ContentType (string) --
The MIME type of the inference returned from the model container.
InvokedProductionVariant (string) --
Identifies the production variant that was invoked.
CustomAttributes (string) --
Provides additional information in the response about the inference returned by 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 return an ID received in the CustomAttributes header of a request or other metadata that a service endpoint was programmed to produce. 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). If the customer wants the custom attribute returned, the model must set the custom attribute to be included on the way back.
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.