docs(client-bedrock): This release of the SDK has the API and documentation for the createcustommodel API. This feature lets you copy a trained model into Amazon Bedrock for inference.

Author: awstools

clients/client-bedrock/src/commands/CreateCustomModelCommand.ts

@@ -28,7 +28,7 @@ export interface CreateCustomModelCommandInput extends CreateCustomModelRequest
 export interface CreateCustomModelCommandOutput extends CreateCustomModelResponse, __MetadataBearer {}
 
 /**
- * <p>Creates a new custom model in Amazon Bedrock from an existing SageMaker AI-trained Amazon Nova model stored in an Amazon-managed Amazon S3 bucket. After the model is active, you can use it for inference.</p> <p>To use the model for inference, you must purchase Provisioned Throughput for it. You can't use On-demand inference with these custom models. For more information about Provisioned Throughput, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html">Provisioned Throughput</a>.</p> <p>The model appears in <code>ListCustomModels</code> with a <code>customizationType</code> of <code>imported</code>. To track the status of the new model, you use the <code>GetCustomModel</code> API operation. The model can be in the following states:</p> <ul> <li> <p> <code>Creating</code> - Initial state during validation and registration</p> </li> <li> <p> <code>Active</code> - Model is ready for use in inference</p> </li> <li> <p> <code>Failed</code> - Creation process encountered an error</p> </li> </ul> <p>For more information about creating custom models, including specific model requirements, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/create-custom-model-from-existing.html">Import a SageMaker AI-trained Amazon Nova model</a> in the Amazon Bedrock User Guide. </p> <note> <p>You use the <code>CreateCustomModel</code> API to import only SageMaker AI-trained Amazon Nova models. To import open-source models, you use the <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateModelImportJob.html">CreateModelImportJob</a>. </p> </note> <p> <b>Related APIs</b> </p> <ul> <li> <p> <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetCustomModel.html">GetCustomModel</a> </p> </li> <li> <p> <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListCustomModels.html">ListCustomModels</a> </p> </li> <li> <p> <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_DeleteCustomModel.html">DeleteCustomModel</a> </p> </li> </ul>
+ * <p>Creates a new custom model in Amazon Bedrock. After the model is active, you can use it for inference.</p> <p>To use the model for inference, you must purchase Provisioned Throughput for it. You can't use On-demand inference with these custom models. For more information about Provisioned Throughput, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html">Provisioned Throughput</a>.</p> <p>The model appears in <code>ListCustomModels</code> with a <code>customizationType</code> of <code>imported</code>. To track the status of the new model, you use the <code>GetCustomModel</code> API operation. The model can be in the following states:</p> <ul> <li> <p> <code>Creating</code> - Initial state during validation and registration</p> </li> <li> <p> <code>Active</code> - Model is ready for use in inference</p> </li> <li> <p> <code>Failed</code> - Creation process encountered an error</p> </li> </ul> <p> <b>Related APIs</b> </p> <ul> <li> <p> <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetCustomModel.html">GetCustomModel</a> </p> </li> <li> <p> <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListCustomModels.html">ListCustomModels</a> </p> </li> <li> <p> <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_DeleteCustomModel.html">DeleteCustomModel</a> </p> </li> </ul>
  * @example
  * Use a bare-bones client and the command you need to make an API call.
  * ```javascript

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

@@ -600,7 +600,7 @@ export interface UpdateMarketplaceModelEndpointResponse {
 }
 
 /**
- * <p>The Amazon S3 data source of the model to import. For the <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateCustomModel.html">CreateCustomModel</a> API operation, you must specify the Amazon S3 URI for the Amazon-managed Amazon S3 bucket containing your model artifacts. SageMaker AI creates this bucket when you run your first SageMaker AI training job.</p>
+ * <p>The Amazon S3 data source of the model to import. </p>
  * @public
  */
 export interface S3DataSource {
@@ -660,7 +660,7 @@ export interface CreateCustomModelRequest {
   modelName: string | undefined;
 
   /**
-   * <p>The data source for the model. The Amazon S3 URI in the model source must be for the Amazon-managed Amazon S3 bucket containing your model artifacts. SageMaker AI creates this bucket when you run your first SageMaker AI training job.</p>
+   * <p>The data source for the model. The Amazon S3 URI in the model source must be for the Amazon-managed Amazon S3 bucket containing your model artifacts.</p>
    * @public
    */
   modelSourceConfig: ModelDataSource | undefined;

codegen/sdk-codegen/aws-models/bedrock.json

@@ -1282,7 +1282,7 @@
         }
       ],
       "traits": {
-        "smithy.api#documentation": "<p>Creates a new custom model in Amazon Bedrock from an existing SageMaker AI-trained Amazon Nova model stored in an Amazon-managed Amazon S3 bucket. After the model is active, you can use it for inference.</p> <p>To use the model for inference, you must purchase Provisioned Throughput for it. You can't use On-demand inference with these custom models. For more information about Provisioned Throughput, see <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html\">Provisioned Throughput</a>.</p> <p>The model appears in <code>ListCustomModels</code> with a <code>customizationType</code> of <code>imported</code>. To track the status of the new model, you use the <code>GetCustomModel</code> API operation. The model can be in the following states:</p> <ul> <li> <p> <code>Creating</code> - Initial state during validation and registration</p> </li> <li> <p> <code>Active</code> - Model is ready for use in inference</p> </li> <li> <p> <code>Failed</code> - Creation process encountered an error</p> </li> </ul> <p>For more information about creating custom models, including specific model requirements, see <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/create-custom-model-from-existing.html\">Import a SageMaker AI-trained Amazon Nova model</a> in the Amazon Bedrock User Guide. </p> <note> <p>You use the <code>CreateCustomModel</code> API to import only SageMaker AI-trained Amazon Nova models. To import open-source models, you use the <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateModelImportJob.html\">CreateModelImportJob</a>. </p> </note> <p> <b>Related APIs</b> </p> <ul> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetCustomModel.html\">GetCustomModel</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListCustomModels.html\">ListCustomModels</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_DeleteCustomModel.html\">DeleteCustomModel</a> </p> </li> </ul>",
+        "smithy.api#documentation": "<p>Creates a new custom model in Amazon Bedrock. After the model is active, you can use it for inference.</p> <p>To use the model for inference, you must purchase Provisioned Throughput for it. You can't use On-demand inference with these custom models. For more information about Provisioned Throughput, see <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html\">Provisioned Throughput</a>.</p> <p>The model appears in <code>ListCustomModels</code> with a <code>customizationType</code> of <code>imported</code>. To track the status of the new model, you use the <code>GetCustomModel</code> API operation. The model can be in the following states:</p> <ul> <li> <p> <code>Creating</code> - Initial state during validation and registration</p> </li> <li> <p> <code>Active</code> - Model is ready for use in inference</p> </li> <li> <p> <code>Failed</code> - Creation process encountered an error</p> </li> </ul> <p> <b>Related APIs</b> </p> <ul> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetCustomModel.html\">GetCustomModel</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListCustomModels.html\">ListCustomModels</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_DeleteCustomModel.html\">DeleteCustomModel</a> </p> </li> </ul>",
         "smithy.api#examples": [
           {
             "title": "Successful CreateCustomModel API call",
@@ -1332,7 +1332,7 @@
         "modelSourceConfig": {
           "target": "com.amazonaws.bedrock#ModelDataSource",
           "traits": {
-            "smithy.api#documentation": "<p>The data source for the model. The Amazon S3 URI in the model source must be for the Amazon-managed Amazon S3 bucket containing your model artifacts. SageMaker AI creates this bucket when you run your first SageMaker AI training job.</p>",
+            "smithy.api#documentation": "<p>The data source for the model. The Amazon S3 URI in the model source must be for the Amazon-managed Amazon S3 bucket containing your model artifacts.</p>",
             "smithy.api#required": {}
           }
         },
@@ -12910,7 +12910,7 @@
         }
       },
       "traits": {
-        "smithy.api#documentation": "<p>The Amazon S3 data source of the model to import. For the <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateCustomModel.html\">CreateCustomModel</a> API operation, you must specify the Amazon S3 URI for the Amazon-managed Amazon S3 bucket containing your model artifacts. SageMaker AI creates this bucket when you run your first SageMaker AI training job.</p>"
+        "smithy.api#documentation": "<p>The Amazon S3 data source of the model to import. </p>"
       }
     },
     "com.amazonaws.bedrock#S3InputFormat": {