feat(client-sagemaker-runtime): Remove incorrect endpoint tests

Author: awstools

clients/client-sagemaker-runtime/README.md

@@ -6,7 +6,7 @@
 
 AWS SDK for JavaScript SageMakerRuntime Client for Node.js, Browser and React Native.
 
-<p> The Amazon SageMaker runtime API. </p>
+<p> The Amazon SageMaker AI runtime API. </p>
 
 ## Installing
 

clients/client-sagemaker-runtime/src/SageMakerRuntime.ts

@@ -76,7 +76,7 @@ export interface SageMakerRuntime {
 }
 
 /**
- * <p> The Amazon SageMaker runtime API. </p>
+ * <p> The Amazon SageMaker AI runtime API. </p>
  * @public
  */
 export class SageMakerRuntime extends SageMakerRuntimeClient implements SageMakerRuntime {}

clients/client-sagemaker-runtime/src/SageMakerRuntimeClient.ts

@@ -293,7 +293,7 @@ export type SageMakerRuntimeClientResolvedConfigType = __SmithyResolvedConfigura
 export interface SageMakerRuntimeClientResolvedConfig extends SageMakerRuntimeClientResolvedConfigType {}
 
 /**
- * <p> The Amazon SageMaker runtime API. </p>
+ * <p> The Amazon SageMaker AI runtime API. </p>
  * @public
  */
 export class SageMakerRuntimeClient extends __Client<

clients/client-sagemaker-runtime/src/commands/InvokeEndpointAsyncCommand.ts

@@ -32,14 +32,14 @@ export interface InvokeEndpointAsyncCommandInput extends InvokeEndpointAsyncInpu
 export interface InvokeEndpointAsyncCommandOutput extends InvokeEndpointAsyncOutput, __MetadataBearer {}
 
 /**
- * <p>After you deploy a model into production using Amazon SageMaker hosting services,
+ * <p>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.</p>
  *          <p>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.</p>
- *          <p>Amazon SageMaker strips all POST headers except those supported by the API. Amazon SageMaker might add
+ *          <p>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. </p>
  *          <p>Calls to <code>InvokeEndpointAsync</code> 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

clients/client-sagemaker-runtime/src/commands/InvokeEndpointCommand.ts

@@ -48,11 +48,11 @@ export type InvokeEndpointCommandOutputType = Omit<InvokeEndpointOutput, "Body">
 export interface InvokeEndpointCommandOutput extends InvokeEndpointCommandOutputType, __MetadataBearer {}
 
 /**
- * <p>After you deploy a model into production using Amazon SageMaker hosting services,
+ * <p>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. </p>
- *          <p>For an overview of Amazon SageMaker, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html">How It Works</a>. </p>
- *          <p>Amazon SageMaker strips all POST headers except those supported by the API. Amazon SageMaker might add
+ *          <p>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>. </p>
+ *          <p>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. </p>
  *          <p>Calls to <code>InvokeEndpoint</code> are authenticated by using Amazon Web Services
@@ -64,7 +64,7 @@ export interface InvokeEndpointCommandOutput extends InvokeEndpointCommandOutput
  *             socket timeout should be set to be 70 seconds.</p>
  *          <note>
  *             <p>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
+ *                 not contain the account ID, but Amazon SageMaker AI determines the account ID from
  *                 the authentication token that is supplied by the caller.</p>
  *          </note>
  * @example

clients/client-sagemaker-runtime/src/commands/InvokeEndpointWithResponseStreamCommand.ts

@@ -49,10 +49,10 @@ export interface InvokeEndpointWithResponseStreamCommandOutput
  * <p>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
+ *             deployed using Amazon SageMaker AI hosting services, and the container for that model
  *             must support inference streaming.</p>
  *          <p>For more information that can help you use this API, see the following sections in the
- *                     <i>Amazon SageMaker Developer Guide</i>:</p>
+ *                     <i>Amazon SageMaker AI Developer Guide</i>:</p>
  *          <ul>
  *             <li>
  *                <p>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>.</p>
@@ -62,9 +62,9 @@ export interface InvokeEndpointWithResponseStreamCommandOutput
  *             </li>
  *          </ul>
  *          <p>Before you can use this operation, your IAM permissions must allow the
- *                 <code>sagemaker:InvokeEndpoint</code> action. For more information about Amazon SageMaker 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</a> in the <i>IAM Service Authorization
+ *                 <code>sagemaker:InvokeEndpoint</code> 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>.</p>
- *          <p>Amazon SageMaker strips all POST headers except those supported by the API. Amazon SageMaker might add
+ *          <p>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. </p>
  *          <p>Calls to <code>InvokeEndpointWithResponseStream</code> are authenticated by using
@@ -132,7 +132,7 @@ export interface InvokeEndpointWithResponseStreamCommandOutput
  *          <dl>
  *             <dt>ModelInvocationTimeExceeded</dt>
  *             <dd>
- *                <p>The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker.</p>
+ *                <p>The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker AI.</p>
  *             </dd>
  *             <dt>StreamBroken</dt>
  *             <dd>

clients/client-sagemaker-runtime/src/index.ts

@@ -1,7 +1,7 @@
 // smithy-typescript generated code
 /* eslint-disable */
 /**
- * <p> The Amazon SageMaker runtime API. </p>
+ * <p> The Amazon SageMaker AI runtime API. </p>
  *
  * @packageDocumentation
  */

clients/client-sagemaker-runtime/src/models/models_0.ts

@@ -61,7 +61,7 @@ export interface InvokeEndpointInput {
 
   /**
    * <p>Provides input data, in the format specified in the <code>ContentType</code>
-   *     request header. Amazon SageMaker passes all of the data in the body to the model. </p>
+   *     request header. Amazon SageMaker AI passes all of the data in the body to the model. </p>
    *          <p>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>.</p>
    * @public
@@ -82,7 +82,7 @@ export interface InvokeEndpointInput {
 
   /**
    * <p>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
+   *         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
@@ -93,7 +93,7 @@ export interface InvokeEndpointInput {
    *         returned. For example, if a custom attribute represents the trace ID, your model can
    *         prepend the custom attribute with <code>Trace ID:</code> in your post-processing
    *         function. </p>
-   *          <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker
+   *          <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker AI
    *         Python SDK. </p>
    * @public
    */
@@ -164,7 +164,7 @@ export interface InvokeEndpointInput {
    *             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 <code>NewSessionId</code> response
-   *             parameter. For any subsequent request where you specify that session ID, SageMaker routes the request to the same instance that supports the session.</p>
+   *             parameter. For any subsequent request where you specify that session ID, SageMaker AI routes the request to the same instance that supports the session.</p>
    * @public
    */
   SessionId?: string | undefined;
@@ -199,7 +199,7 @@ export interface InvokeEndpointOutput {
 
   /**
    * <p>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
+   *         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 <code>CustomAttributes</code> 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
@@ -212,7 +212,7 @@ export interface InvokeEndpointOutput {
    *         returned. For example, if a custom attribute represents the trace ID, your model can
    *         prepend the custom attribute with <code>Trace ID:</code> in your post-processing
    *         function.</p>
-   *          <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker
+   *          <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker AI
    *         Python SDK.</p>
    * @public
    */
@@ -368,7 +368,7 @@ export interface InvokeEndpointAsyncInput {
 
   /**
    * <p>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
+   *         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
@@ -379,14 +379,14 @@ export interface InvokeEndpointAsyncInput {
    *         returned. For example, if a custom attribute represents the trace ID, your model can
    *         prepend the custom attribute with <code>Trace ID:</code> in your post-processing
    *         function. </p>
-   *          <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker
+   *          <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker AI
    *         Python SDK. </p>
    * @public
    */
   CustomAttributes?: string | undefined;
 
   /**
-   * <p>The identifier for the inference request. Amazon SageMaker will generate an
+   * <p>The identifier for the inference request. Amazon SageMaker AI will generate an
    *             identifier for you if none is specified. </p>
    * @public
    */
@@ -419,7 +419,7 @@ export interface InvokeEndpointAsyncInput {
 export interface InvokeEndpointAsyncOutput {
   /**
    * <p>Identifier for an inference request. This will be the same as the
-   *                 <code>InferenceId</code> specified in the input. Amazon SageMaker will generate
+   *                 <code>InferenceId</code> specified in the input. Amazon SageMaker AI will generate
    *             an identifier for you if you do not specify one.</p>
    * @public
    */
@@ -474,7 +474,7 @@ export interface InvokeEndpointWithResponseStreamInput {
 
   /**
    * <p>Provides input data, in the format specified in the <code>ContentType</code>
-   *     request header. Amazon SageMaker passes all of the data in the body to the model. </p>
+   *     request header. Amazon SageMaker AI passes all of the data in the body to the model. </p>
    *          <p>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>.</p>
    * @public
@@ -495,7 +495,7 @@ export interface InvokeEndpointWithResponseStreamInput {
 
   /**
    * <p>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
+   *         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
@@ -506,7 +506,7 @@ export interface InvokeEndpointWithResponseStreamInput {
    *         returned. For example, if a custom attribute represents the trace ID, your model can
    *         prepend the custom attribute with <code>Trace ID:</code> in your post-processing
    *         function. </p>
-   *          <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker
+   *          <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker AI
    *         Python SDK. </p>
    * @public
    */
@@ -565,7 +565,7 @@ export interface InvokeEndpointWithResponseStreamInput {
  *          <dl>
  *             <dt>ModelInvocationTimeExceeded</dt>
  *             <dd>
- *                <p>The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker.</p>
+ *                <p>The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker AI.</p>
  *             </dd>
  *             <dt>StreamBroken</dt>
  *             <dd>
@@ -585,7 +585,7 @@ export class ModelStreamError extends __BaseException {
    *             <dt>ModelInvocationTimeExceeded</dt>
    *             <dd>
    *                <p>The model failed to finish sending the response within the timeout period
-   *                         allowed by Amazon SageMaker.</p>
+   *                         allowed by Amazon SageMaker AI.</p>
    *             </dd>
    *             <dt>StreamBroken</dt>
    *             <dd>
@@ -659,7 +659,7 @@ export namespace ResponseStream {
    *          <dl>
    *             <dt>ModelInvocationTimeExceeded</dt>
    *             <dd>
-   *                <p>The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker.</p>
+   *                <p>The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker AI.</p>
    *             </dd>
    *             <dt>StreamBroken</dt>
    *             <dd>
@@ -737,7 +737,7 @@ export interface InvokeEndpointWithResponseStreamOutput {
 
   /**
    * <p>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
+   *         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 <code>CustomAttributes</code> 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
@@ -750,7 +750,7 @@ export interface InvokeEndpointWithResponseStreamOutput {
    *         returned. For example, if a custom attribute represents the trace ID, your model can
    *         prepend the custom attribute with <code>Trace ID:</code> in your post-processing
    *         function.</p>
-   *          <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker
+   *          <p>This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker AI
    *         Python SDK.</p>
    * @public
    */