feat(client-sagemaker): Added ModelNameEquals, ModelPackageVersionArn…

…Equals in request and ModelName, SamplePayloadUrl, ModelPackageVersionArn in response of ListInferenceRecommendationsJobs API. Added Invocation timestamps in response of DescribeInferenceRecommendationsJob API & ListInferenceRecommendationsJobSteps API.

clients/client-sagemaker/src/commands/DescribeInferenceRecommendationsJobCommand.ts

@@ -171,6 +171,8 @@ export interface DescribeInferenceRecommendationsJobCommandOutput
  * //         CompilationJobName: "STRING_VALUE",
  * //       },
  * //       RecommendationId: "STRING_VALUE",
+ * //       InvocationEndTime: new Date("TIMESTAMP"),
+ * //       InvocationStartTime: new Date("TIMESTAMP"),
  * //     },
  * //   ],
  * //   EndpointPerformances: [ // EndpointPerformances

clients/client-sagemaker/src/commands/ListInferenceRecommendationsJobStepsCommand.ts

@@ -98,6 +98,8 @@ export interface ListInferenceRecommendationsJobStepsCommandOutput
  * //           MaxInvocations: Number("int"), // required
  * //           ModelLatency: Number("int"), // required
  * //         },
+ * //         InvocationEndTime: new Date("TIMESTAMP"),
+ * //         InvocationStartTime: new Date("TIMESTAMP"),
  * //       },
  * //     },
  * //   ],

clients/client-sagemaker/src/commands/ListInferenceRecommendationsJobsCommand.ts

@@ -59,6 +59,8 @@ export interface ListInferenceRecommendationsJobsCommandOutput
  *   SortOrder: "Ascending" || "Descending",
  *   NextToken: "STRING_VALUE",
  *   MaxResults: Number("int"),
+ *   ModelNameEquals: "STRING_VALUE",
+ *   ModelPackageVersionArnEquals: "STRING_VALUE",
  * };
  * const command = new ListInferenceRecommendationsJobsCommand(input);
  * const response = await client.send(command);
@@ -75,6 +77,9 @@ export interface ListInferenceRecommendationsJobsCommandOutput
  * //       RoleArn: "STRING_VALUE", // required
  * //       LastModifiedTime: new Date("TIMESTAMP"), // required
  * //       FailureReason: "STRING_VALUE",
+ * //       ModelName: "STRING_VALUE",
+ * //       SamplePayloadUrl: "STRING_VALUE",
+ * //       ModelPackageVersionArn: "STRING_VALUE",
  * //     },
  * //   ],
  * //   NextToken: "STRING_VALUE",

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

@@ -1058,6 +1058,7 @@ export type S3DataType = (typeof S3DataType)[keyof typeof S3DataType];
 /**
  * @public
  * <p>Describes the S3 data source.</p>
+ *          <p>Your input bucket must be in the same Amazon Web Services region as your training job.</p>
  */
 export interface S3DataSource {
   /**
@@ -1131,6 +1132,7 @@ export interface S3DataSource {
    *                     your behalf. </p>
    *             </li>
    *          </ul>
+   *          <p>Your input bucket must be located in same Amazon Web Services region as your training job.</p>
    */
   S3Uri: string | undefined;
 

clients/client-sagemaker/src/models/models_1.ts

@@ -6662,7 +6662,7 @@ export interface SourceAlgorithm {
    *             This path must point to a single <code>gzip</code> compressed tar archive
    *                 (<code>.tar.gz</code> suffix).</p>
    *          <note>
-   *             <p>The model artifacts must be in an S3 bucket that is in the same region as the
+   *             <p>The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the
    *                 algorithm.</p>
    *          </note>
    */
@@ -8776,6 +8776,7 @@ export interface CreateTrainingJobRequest {
    *             data files from an S3 bucket to a local directory in the Docker container, or makes it
    *             available as input streams. For example, if you specify an EFS location, input data
    *             files are available as input streams. They do not need to be downloaded.</p>
+   *          <p>Your input must be in the same Amazon Web Services region as your training job.</p>
    */
   InputDataConfig?: Channel[];
 

clients/client-sagemaker/src/models/models_2.ts

@@ -4236,6 +4236,16 @@ export interface InferenceRecommendation {
    * <p>The recommendation ID which uniquely identifies each recommendation.</p>
    */
   RecommendationId?: string;
+
+  /**
+   * <p>A timestamp that shows when the benchmark completed.</p>
+   */
+  InvocationEndTime?: Date;
+
+  /**
+   * <p>A timestamp that shows when the benchmark started.</p>
+   */
+  InvocationStartTime?: Date;
 }
 
 /**
@@ -10524,6 +10534,22 @@ export interface InferenceRecommendationsJob {
    * <p>If the job fails, provides information why the job failed.</p>
    */
   FailureReason?: string;
+
+  /**
+   * <p>The name of the created model.</p>
+   */
+  ModelName?: string;
+
+  /**
+   * <p>The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored.
+   *          This path must point to a single gzip compressed tar archive (.tar.gz suffix).</p>
+   */
+  SamplePayloadUrl?: string;
+
+  /**
+   * <p>The Amazon Resource Name (ARN) of a versioned model package.</p>
+   */
+  ModelPackageVersionArn?: string;
 }
 
 /**
@@ -10555,6 +10581,16 @@ export interface RecommendationJobInferenceBenchmark {
    * <p>The metrics for an existing endpoint compared in an Inference Recommender job.</p>
    */
   EndpointMetrics?: InferenceMetrics;
+
+  /**
+   * <p>A timestamp that shows when the benchmark completed.</p>
+   */
+  InvocationEndTime?: Date;
+
+  /**
+   * <p>A timestamp that shows when the benchmark started.</p>
+   */
+  InvocationStartTime?: Date;
 }
 
 /**

clients/client-sagemaker/src/models/models_3.ts

@@ -2670,6 +2670,16 @@ export interface ListInferenceRecommendationsJobsRequest {
    * <p>The maximum number of recommendations to return in the response.</p>
    */
   MaxResults?: number;
+
+  /**
+   * <p>A filter that returns only jobs that were created for this model.</p>
+   */
+  ModelNameEquals?: string;
+
+  /**
+   * <p>A filter that returns only jobs that were created for this versioned model package.</p>
+   */
+  ModelPackageVersionArnEquals?: string;
 }
 
 /**
@@ -8782,6 +8792,7 @@ export interface TrainingJob {
   /**
    * <p>An array of <code>Channel</code> objects that describes each data input
    *             channel.</p>
+   *          <p>Your input must be in the same Amazon Web Services region as your training job.</p>
    */
   InputDataConfig?: Channel[];
 

clients/client-sagemaker/src/protocols/Aws_json1_1.ts

@@ -20631,6 +20631,8 @@ const se_ListInferenceRecommendationsJobsRequest = (
     LastModifiedTimeAfter: (_) => Math.round(_.getTime() / 1000),
     LastModifiedTimeBefore: (_) => Math.round(_.getTime() / 1000),
     MaxResults: [],
+    ModelNameEquals: [],
+    ModelPackageVersionArnEquals: [],
     NameContains: [],
     NextToken: [],
     SortBy: [],
@@ -25028,6 +25030,8 @@ const de_InferenceExperimentSummary = (output: any, context: __SerdeContext): In
 const de_InferenceRecommendation = (output: any, context: __SerdeContext): InferenceRecommendation => {
   return take(output, {
     EndpointConfiguration: _json,
+    InvocationEndTime: (_: any) => __expectNonNull(__parseEpochTimestamp(__expectNumber(_))),
+    InvocationStartTime: (_: any) => __expectNonNull(__parseEpochTimestamp(__expectNumber(_))),
     Metrics: (_: any) => de_RecommendationMetrics(_, context),
     ModelConfiguration: _json,
     RecommendationId: __expectString,
@@ -25059,7 +25063,10 @@ const de_InferenceRecommendationsJob = (output: any, context: __SerdeContext): I
     JobName: __expectString,
     JobType: __expectString,
     LastModifiedTime: (_: any) => __expectNonNull(__parseEpochTimestamp(__expectNumber(_))),
+    ModelName: __expectString,
+    ModelPackageVersionArn: __expectString,
     RoleArn: __expectString,
+    SamplePayloadUrl: __expectString,
     Status: __expectString,
   }) as any;
 };
@@ -27326,6 +27333,8 @@ const de_RecommendationJobInferenceBenchmark = (
     EndpointConfiguration: _json,
     EndpointMetrics: _json,
     FailureReason: __expectString,
+    InvocationEndTime: (_: any) => __expectNonNull(__parseEpochTimestamp(__expectNumber(_))),
+    InvocationStartTime: (_: any) => __expectNonNull(__parseEpochTimestamp(__expectNumber(_))),
     Metrics: (_: any) => de_RecommendationMetrics(_, context),
     ModelConfiguration: _json,
   }) as any;