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.
Endpoints are scoped to an individual account, and are not public. The URL does not contain the account ID, but Amazon SageMaker determines the account ID from the authentication token that is supplied by the caller.
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.
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.
Inference requests sent to this API are enqueued for asynchronous processing. The processing of the inference request may or may not complete before you receive a response from this API. The response from this API will not contain the result of the inference request but contain information about where you can locate it.
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 InvokeEndpointAsync 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.
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 in an asynchronous manner.
Inference requests sent to this API are enqueued for asynchronous processing. The processing of the inference request may or may not complete before you receive a response from this API. The response from this API will not contain the result of the inference request but contain information about where you can locate it.
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 InvokeEndpointAsync 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.
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.
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 AI 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 AI 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 AI actions for IAM policies, see Actions, resources, and condition keys for Amazon SageMaker AI in the IAM Service Authorization Reference.
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 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.
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.
", + "documentation":"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.
", "location":"header", "locationName":"X-Amzn-SageMaker-Custom-Attributes" }, "InferenceId":{ "shape":"InferenceId", - "documentation":"The identifier for the inference request. Amazon SageMaker will generate an identifier for you if none is specified.
", + "documentation":"The identifier for the inference request. Amazon SageMaker AI will generate an identifier for you if none is specified.
", "location":"header", "locationName":"X-Amzn-SageMaker-Inference-Id" }, @@ -212,7 +212,7 @@ "members":{ "InferenceId":{ "shape":"Header", - "documentation":"Identifier for an inference request. This will be the same as the InferenceId specified in the input. Amazon SageMaker will generate an identifier for you if you do not specify one.
Identifier for an inference request. This will be the same as the InferenceId specified in the input. Amazon SageMaker AI will generate an identifier for you if you do not specify one.
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.
" + "documentation":"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.
" }, "ContentType":{ "shape":"Header", @@ -259,7 +259,7 @@ }, "CustomAttributes":{ "shape":"CustomAttributesHeader", - "documentation":"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.
", + "documentation":"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.
", "location":"header", "locationName":"X-Amzn-SageMaker-Custom-Attributes" }, @@ -301,7 +301,7 @@ }, "SessionId":{ "shape":"SessionIdOrNewSessionConstantHeader", - "documentation":"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.
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.
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.
", + "documentation":"Provides additional information in the response about the inference returned by 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 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 AI Python SDK.
", "location":"header", "locationName":"X-Amzn-SageMaker-Custom-Attributes" }, @@ -364,7 +364,7 @@ }, "Body":{ "shape":"BodyBlob", - "documentation":"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.
" + "documentation":"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.
" }, "ContentType":{ "shape":"Header", @@ -380,7 +380,7 @@ }, "CustomAttributes":{ "shape":"CustomAttributesHeader", - "documentation":"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.
", + "documentation":"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.
", "location":"header", "locationName":"X-Amzn-SageMaker-Custom-Attributes" }, @@ -436,7 +436,7 @@ }, "CustomAttributes":{ "shape":"CustomAttributesHeader", - "documentation":"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.
", + "documentation":"Provides additional information in the response about the inference returned by 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 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 AI Python SDK.
", "location":"header", "locationName":"X-Amzn-SageMaker-Custom-Attributes" } @@ -485,10 +485,10 @@ "Message":{"shape":"Message"}, "ErrorCode":{ "shape":"ErrorCode", - "documentation":"This error can have the following error codes:
The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker.
The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.
This error can have the following error codes:
The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker AI.
The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.
An error occurred while streaming the response body. This error can have the following error codes:
The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker.
The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.
An error occurred while streaming the response body. This error can have the following error codes:
The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker AI.
The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.
An error occurred while streaming the response body. This error can have the following error codes:
The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker.
The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.
An error occurred while streaming the response body. This error can have the following error codes:
The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker AI.
The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.
The Amazon SageMaker runtime API.
" + "documentation":"The Amazon SageMaker AI runtime API.
" } diff --git a/src/sagemaker_core/main/resources.py b/src/sagemaker_core/main/resources.py index d5ce542a..c4d4e394 100644 --- a/src/sagemaker_core/main/resources.py +++ b/src/sagemaker_core/main/resources.py @@ -9426,20 +9426,20 @@ def invoke( region: Optional[str] = None, ) -> Optional[shapes.InvokeEndpointOutput]: """ - 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. + 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. Parameters: - body: 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. + body: 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. content_type: The MIME type of the input data in the request body. accept: The desired MIME type of the inference response from the model container. - custom_attributes: 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. + custom_attributes: 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. target_model: The model to request for inference when invoking a multi-model endpoint. target_variant: 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 target_container_hostname: If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke. inference_id: 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. enable_explanations: 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. inference_component_name: If the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke. - session_id: 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. + session_id: 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. session: Boto3 session. region: Region name. @@ -9507,14 +9507,14 @@ def invoke_async( region: Optional[str] = None, ) -> Optional[shapes.InvokeEndpointAsyncOutput]: """ - 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. + 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 in an asynchronous manner. Parameters: input_location: The Amazon S3 URI where the inference request payload is stored. content_type: The MIME type of the input data in the request body. accept: The desired MIME type of the inference response from the model container. - custom_attributes: 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. - inference_id: The identifier for the inference request. Amazon SageMaker will generate an identifier for you if none is specified. + custom_attributes: 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. + inference_id: The identifier for the inference request. Amazon SageMaker AI will generate an identifier for you if none is specified. request_ttl_seconds: 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. invocation_timeout_seconds: 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. session: Boto3 session. @@ -9582,10 +9582,10 @@ def invoke_with_response_stream( Invokes a model at the specified endpoint to return the inference response as a stream. Parameters: - body: 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. + body: 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. content_type: The MIME type of the input data in the request body. accept: The desired MIME type of the inference response from the model container. - custom_attributes: 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. + custom_attributes: 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. target_variant: 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 target_container_hostname: If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke. inference_id: An identifier that you assign to your request. @@ -9610,7 +9610,7 @@ def invoke_with_response_stream( InternalFailure: An internal failure occurred. Try your request again. If the problem persists, contact Amazon Web Services customer support. InternalStreamFailure: The stream processing failed because of an unknown error, exception or failure. Try your request again. ModelError: Model (owned by the customer in the container) returned 4xx or 5xx error code. - ModelStreamError: 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. + ModelStreamError: 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 AI. StreamBroken The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed. ServiceUnavailable: The service is currently unavailable. ValidationError: There was an error validating your request. """ diff --git a/src/sagemaker_core/main/shapes.py b/src/sagemaker_core/main/shapes.py index 0ca0c269..057c3956 100644 --- a/src/sagemaker_core/main/shapes.py +++ b/src/sagemaker_core/main/shapes.py @@ -66,7 +66,7 @@ class InvokeEndpointAsyncOutput(Base): Attributes ---------------------- - inference_id: Identifier for an inference request. This will be the same as the InferenceId specified in the input. Amazon SageMaker will generate an identifier for you if you do not specify one. + inference_id: Identifier for an inference request. This will be the same as the InferenceId specified in the input. Amazon SageMaker AI will generate an identifier for you if you do not specify one. output_location: The Amazon S3 URI where the inference response payload is stored. failure_location: The Amazon S3 URI where the inference failure response payload is stored. """ @@ -85,7 +85,7 @@ class InvokeEndpointOutput(Base): body: 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. content_type: The MIME type of the inference returned from the model container. invoked_production_variant: Identifies the production variant that was invoked. - custom_attributes: 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. + custom_attributes: Provides additional information in the response about the inference returned by 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 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 AI Python SDK. new_session_id: If you created a stateful session with your request, the ID and expiration time that the model assigns to that session. closed_session_id: If you closed a stateful session with your request, the ID of that session. """ @@ -114,12 +114,12 @@ class PayloadPart(Base): class ModelStreamError(Base): """ ModelStreamError - 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. + 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 AI. StreamBroken The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed. Attributes ---------------------- message - error_code: 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. + error_code: 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 AI. StreamBroken The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed. """ message: Optional[str] = Unassigned() @@ -134,7 +134,7 @@ class ResponseStream(Base): Attributes ---------------------- payload_part: 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. - model_stream_error: 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. + model_stream_error: 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 AI. StreamBroken The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed. internal_stream_failure: The stream processing failed because of an unknown error, exception or failure. Try your request again. """ @@ -152,7 +152,7 @@ class InvokeEndpointWithResponseStreamOutput(Base): body content_type: The MIME type of the inference returned from the model container. invoked_production_variant: Identifies the production variant that was invoked. - custom_attributes: 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. + custom_attributes: Provides additional information in the response about the inference returned by 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 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 AI Python SDK. """ body: ResponseStream diff --git a/tst/tools/test_resources_codegen.py b/tst/tools/test_resources_codegen.py index 76d465dd..9aa5cb1a 100644 --- a/tst/tools/test_resources_codegen.py +++ b/tst/tools/test_resources_codegen.py @@ -1,7 +1,8 @@ import json + +from sagemaker_core.tools.constants import SERVICE_JSON_FILE_PATH from sagemaker_core.tools.method import Method from sagemaker_core.tools.resources_codegen import ResourcesCodeGen -from sagemaker_core.tools.constants import SERVICE_JSON_FILE_PATH class TestGenerateResource: @@ -809,20 +810,20 @@ def invoke( region: Optional[str] = None, ) -> Optional[shapes.InvokeEndpointOutput]: """ - 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. + 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. Parameters: - body: 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. + body: 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. content_type: The MIME type of the input data in the request body. accept: The desired MIME type of the inference response from the model container. - custom_attributes: 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. + custom_attributes: 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. target_model: The model to request for inference when invoking a multi-model endpoint. target_variant: 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 target_container_hostname: If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke. inference_id: 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. enable_explanations: 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. inference_component_name: If the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke. - session_id: 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. + session_id: 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. session: Boto3 session. region: Region name. @@ -905,14 +906,14 @@ def invoke_async( region: Optional[str] = None, ) -> Optional[shapes.InvokeEndpointAsyncOutput]: """ - 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. + 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 in an asynchronous manner. Parameters: input_location: The Amazon S3 URI where the inference request payload is stored. content_type: The MIME type of the input data in the request body. accept: The desired MIME type of the inference response from the model container. - custom_attributes: 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. - inference_id: The identifier for the inference request. Amazon SageMaker will generate an identifier for you if none is specified. + custom_attributes: 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. + inference_id: The identifier for the inference request. Amazon SageMaker AI will generate an identifier for you if none is specified. request_ttl_seconds: 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. invocation_timeout_seconds: 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. session: Boto3 session. @@ -995,10 +996,10 @@ def invoke_with_response_stream( Invokes a model at the specified endpoint to return the inference response as a stream. Parameters: - body: 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. + body: 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. content_type: The MIME type of the input data in the request body. accept: The desired MIME type of the inference response from the model container. - custom_attributes: 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. + custom_attributes: 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. target_variant: 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 target_container_hostname: If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke. inference_id: An identifier that you assign to your request. @@ -1023,7 +1024,7 @@ def invoke_with_response_stream( InternalFailure: An internal failure occurred. Try your request again. If the problem persists, contact Amazon Web Services customer support. InternalStreamFailure: The stream processing failed because of an unknown error, exception or failure. Try your request again. ModelError: Model (owned by the customer in the container) returned 4xx or 5xx error code. - ModelStreamError: 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. + ModelStreamError: 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 AI. StreamBroken The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed. ServiceUnavailable: The service is currently unavailable. ValidationError: There was an error validating your request. """