<?xml version="1.0"?>
<doc>
    <assembly>
        <name>AWS.Tools.SageMakerRuntime</name>
    </assembly>
    <members>
        <member name="T:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet">
            <summary>
            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. 
            
             
            <para>
            For an overview of Amazon SageMaker AI, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html">How
            It Works</a>. 
            </para><para>
            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. 
            </para><para>
            Calls to <c>InvokeEndpoint</c> are authenticated by using Amazon Web Services Signature
            Version 4. For information, see <a href="https://docs.aws.amazon.com/AmazonS3/latest/API/sig-v4-authenticating-requests.html">Authenticating
            Requests (Amazon Web Services Signature Version 4)</a> in the <i>Amazon S3 API Reference</i>.
            </para><para>
            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.
            </para><note><para>
            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.
            </para></note>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.Accept">
            <summary>
            <para>
            <para>The desired MIME type of the inference response from the model container.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.Body">
            <summary>
            <para>
            <para>Provides input data, in the format specified in the <c>ContentType</c> request header.
            Amazon SageMaker AI passes all of the data in the body to the model. </para><para>For information about the format of the request body, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html">Common
            Data Formats-Inference</a>.</para>
            </para>
            <para>The cmdlet will automatically convert the supplied parameter of type string, string[], System.IO.FileInfo or System.IO.Stream to byte[] before supplying it to the service.</para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.ContentType">
            <summary>
            <para>
            <para>The MIME type of the input data in the request body.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.CustomAttribute">
            <summary>
            <para>
            <para>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 <a href="https://datatracker.ietf.org/doc/html/rfc7230#section-3.2.6">Section
            3.3.6. Field Value Components</a> of the Hypertext Transfer Protocol (HTTP/1.1). </para><para>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 <c>Trace ID:</c> in your post-processing function.
            </para><para>This feature is currently supported in the Amazon Web Services SDKs but not in the
            Amazon SageMaker AI Python SDK. </para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.EnableExplanation">
            <summary>
            <para>
            <para>An optional JMESPath expression used to override the <c>EnableExplanations</c> parameter
            of the <c>ClarifyExplainerConfig</c> API. See the <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable">EnableExplanations</a>
            section in the developer guide for more information. </para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.EndpointName">
            <summary>
            <para>
            <para>The name of the endpoint that you specified when you created the endpoint using the
            <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpoint.html">CreateEndpoint</a>
            API.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.InferenceComponentName">
            <summary>
            <para>
            <para>If the endpoint hosts one or more inference components, this parameter specifies the
            name of inference component to invoke.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.InferenceId">
            <summary>
            <para>
            <para>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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture.html">Capture
            Data</a>.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.SessionId">
            <summary>
            <para>
            <para>Creates a stateful session or identifies an existing one. You can do one of the following:</para><ul><li><para>Create a stateful session by specifying the value <c>NEW_SESSION</c>.</para></li><li><para>Send your request to an existing stateful session by specifying the ID of that session.</para></li></ul><para>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 <c>NewSessionId</c> 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.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.TargetContainerHostname">
            <summary>
            <para>
            <para>If the endpoint hosts multiple containers and is configured to use direct invocation,
            this parameter specifies the host name of the container to invoke.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.TargetModel">
            <summary>
            <para>
            <para>The model to request for inference when invoking a multi-model endpoint.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.TargetVariant">
            <summary>
            <para>
            <para>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.</para><para>For information about how to use variant targeting to perform a/b testing, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html">Test
            models in production</a></para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.Select">
            <summary>
            Use the -Select parameter to control the cmdlet output. The default value is '*'.
            Specifying -Select '*' will result in the cmdlet returning the whole service response (Amazon.SageMakerRuntime.Model.InvokeEndpointResponse).
            Specifying the name of a property of type Amazon.SageMakerRuntime.Model.InvokeEndpointResponse will result in that property being returned.
            Specifying -Select '^ParameterName' will result in the cmdlet returning the selected cmdlet parameter value.
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.PassThru">
            <summary>
            Changes the cmdlet behavior to return the value passed to the EndpointName parameter.
            The -PassThru parameter is deprecated, use -Select '^EndpointName' instead. This parameter will be removed in a future version.
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointCmdlet.Force">
            <summary>
            This parameter overrides confirmation prompts to force 
            the cmdlet to continue its operation. This parameter should always
            be used with caution.
            </summary>
        </member>
        <member name="T:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet">
            <summary>
            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.
            
             
            <para>
            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.
            </para><para>
            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. 
            </para><para>
            Calls to <c>InvokeEndpointAsync</c> are authenticated by using Amazon Web Services
            Signature Version 4. For information, see <a href="https://docs.aws.amazon.com/AmazonS3/latest/API/sig-v4-authenticating-requests.html">Authenticating
            Requests (Amazon Web Services Signature Version 4)</a> in the <i>Amazon S3 API Reference</i>.
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.Accept">
            <summary>
            <para>
            <para>The desired MIME type of the inference response from the model container.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.ContentType">
            <summary>
            <para>
            <para>The MIME type of the input data in the request body.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.CustomAttribute">
            <summary>
            <para>
            <para>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 <a href="https://datatracker.ietf.org/doc/html/rfc7230#section-3.2.6">Section
            3.3.6. Field Value Components</a> of the Hypertext Transfer Protocol (HTTP/1.1). </para><para>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 <c>Trace ID:</c> in your post-processing function.
            </para><para>This feature is currently supported in the Amazon Web Services SDKs but not in the
            Amazon SageMaker AI Python SDK. </para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.EndpointName">
            <summary>
            <para>
            <para>The name of the endpoint that you specified when you created the endpoint using the
            <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpoint.html">CreateEndpoint</a>
            API.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.Filename">
            <summary>
            <para>
            <para>The filename for the inference response payload stored in Amazon S3. If not specified,
            Amazon SageMaker AI generates a filename based on the inference ID.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.InferenceId">
            <summary>
            <para>
            <para>The identifier for the inference request. Amazon SageMaker AI will generate an identifier
            for you if none is specified. </para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.InputLocation">
            <summary>
            <para>
            <para>The Amazon S3 URI where the inference request payload is stored.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.InvocationTimeoutSecond">
            <summary>
            <para>
            <para>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.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.RequestTTLSecond">
            <summary>
            <para>
            <para>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.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.S3OutputPathExtension">
            <summary>
            <para>
            <para>The path extension that is appended to the Amazon S3 output path where the inference
            response payload is stored.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.Select">
            <summary>
            Use the -Select parameter to control the cmdlet output. The default value is '*'.
            Specifying -Select '*' will result in the cmdlet returning the whole service response (Amazon.SageMakerRuntime.Model.InvokeEndpointAsyncResponse).
            Specifying the name of a property of type Amazon.SageMakerRuntime.Model.InvokeEndpointAsyncResponse will result in that property being returned.
            Specifying -Select '^ParameterName' will result in the cmdlet returning the selected cmdlet parameter value.
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.PassThru">
            <summary>
            Changes the cmdlet behavior to return the value passed to the EndpointName parameter.
            The -PassThru parameter is deprecated, use -Select '^EndpointName' instead. This parameter will be removed in a future version.
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointAsyncCmdlet.Force">
            <summary>
            This parameter overrides confirmation prompts to force 
            the cmdlet to continue its operation. This parameter should always
            be used with caution.
            </summary>
        </member>
        <member name="T:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet">
            <summary>
            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.
            
             
            <para>
            For more information that can help you use this API, see the following sections in
            the <i>Amazon SageMaker AI Developer Guide</i>:
            </para><ul><li><para>
            For information about how to add streaming support to a model, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-code-how-containe-serves-requests">How
            Containers Serve Requests</a>.
            </para></li><li><para>
            For information about how to process the streaming response, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints-test-endpoints.html">Invoke
            real-time endpoints</a>.
            </para></li></ul><para>
            Before you can use this operation, your IAM permissions must allow the <c>sagemaker:InvokeEndpoint</c>
            action. For more information about Amazon SageMaker AI actions for IAM policies, see
            <a href="https://docs.aws.amazon.com/service-authorization/latest/reference/list_amazonsagemaker.html">Actions,
            resources, and condition keys for Amazon SageMaker AI</a> in the <i>IAM Service Authorization
            Reference</i>.
            </para><para>
            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. 
            </para><para>
            Calls to <c>InvokeEndpointWithResponseStream</c> are authenticated by using Amazon
            Web Services Signature Version 4. For information, see <a href="https://docs.aws.amazon.com/AmazonS3/latest/API/sig-v4-authenticating-requests.html">Authenticating
            Requests (Amazon Web Services Signature Version 4)</a> in the <i>Amazon S3 API Reference</i>.
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.Accept">
            <summary>
            <para>
            <para>The desired MIME type of the inference response from the model container.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.Body">
            <summary>
            <para>
            <para>Provides input data, in the format specified in the <c>ContentType</c> request header.
            Amazon SageMaker AI passes all of the data in the body to the model. </para><para>For information about the format of the request body, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html">Common
            Data Formats-Inference</a>.</para>
            </para>
            <para>The cmdlet will automatically convert the supplied parameter of type string, string[], System.IO.FileInfo or System.IO.Stream to byte[] before supplying it to the service.</para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.ContentType">
            <summary>
            <para>
            <para>The MIME type of the input data in the request body.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.CustomAttribute">
            <summary>
            <para>
            <para>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 <a href="https://datatracker.ietf.org/doc/html/rfc7230#section-3.2.6">Section
            3.3.6. Field Value Components</a> of the Hypertext Transfer Protocol (HTTP/1.1). </para><para>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 <c>Trace ID:</c> in your post-processing function.
            </para><para>This feature is currently supported in the Amazon Web Services SDKs but not in the
            Amazon SageMaker AI Python SDK. </para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.EndpointName">
            <summary>
            <para>
            <para>The name of the endpoint that you specified when you created the endpoint using the
            <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpoint.html">CreateEndpoint</a>
            API.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.InferenceComponentName">
            <summary>
            <para>
            <para>If the endpoint hosts one or more inference components, this parameter specifies the
            name of inference component to invoke for a streaming response.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.InferenceId">
            <summary>
            <para>
            <para>An identifier that you assign to your request.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.SessionId">
            <summary>
            <para>
            <para>The ID of a stateful session to handle your request.</para><para>You can't create a stateful session by using the <c>InvokeEndpointWithResponseStream</c>
            action. Instead, you can create one by using the <c><a>InvokeEndpoint</a></c> action.
            In your request, you specify <c>NEW_SESSION</c> for the <c>SessionId</c> request parameter.
            The response to that request provides the session ID for the <c>NewSessionId</c> response
            parameter.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.TargetContainerHostname">
            <summary>
            <para>
            <para>If the endpoint hosts multiple containers and is configured to use direct invocation,
            this parameter specifies the host name of the container to invoke.</para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.TargetVariant">
            <summary>
            <para>
            <para>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.</para><para>For information about how to use variant targeting to perform a/b testing, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html">Test
            models in production</a></para>
            </para>
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.Select">
            <summary>
            Use the -Select parameter to control the cmdlet output. The default value is '*'.
            Specifying -Select '*' will result in the cmdlet returning the whole service response (Amazon.SageMakerRuntime.Model.InvokeEndpointWithResponseStreamResponse).
            Specifying the name of a property of type Amazon.SageMakerRuntime.Model.InvokeEndpointWithResponseStreamResponse will result in that property being returned.
            Specifying -Select '^ParameterName' will result in the cmdlet returning the selected cmdlet parameter value.
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.PassThru">
            <summary>
            Changes the cmdlet behavior to return the value passed to the EndpointName parameter.
            The -PassThru parameter is deprecated, use -Select '^EndpointName' instead. This parameter will be removed in a future version.
            </summary>
        </member>
        <member name="P:Amazon.PowerShell.Cmdlets.SMR.InvokeSMREndpointWithResponseStreamCmdlet.Force">
            <summary>
            This parameter overrides confirmation prompts to force 
            the cmdlet to continue its operation. This parameter should always
            be used with caution.
            </summary>
        </member>
    </members>
</doc>
