feat(client-sagemaker): Amazon SageMaker Autopilot adds support for custom validation dataset and validation ratio through the CreateAutoMLJob and DescribeAutoMLJob APIs.

Author: awstools

clients/client-sagemaker/README.md

@@ -7,12 +7,12 @@
 
 AWS SDK for JavaScript SageMaker Client for Node.js, Browser and React Native.
 
-<p>Provides APIs for creating and managing Amazon SageMaker resources. </p>
+<p>Provides APIs for creating and managing SageMaker resources. </p>
 <p>Other Resources:</p>
 <ul>
 <li>
 <p>
-<a href="https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html#first-time-user">Amazon SageMaker Developer
+<a href="https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html#first-time-user">SageMaker Developer
 Guide</a>
 </p>
 </li>

clients/client-sagemaker/src/SageMaker.ts

@@ -1339,12 +1339,12 @@ type SageMakerClientResolvedConfigType = __SmithyResolvedConfiguration<__HttpHan
 export interface SageMakerClientResolvedConfig extends SageMakerClientResolvedConfigType {}
 
 /**
- * <p>Provides APIs for creating and managing Amazon SageMaker resources. </p>
+ * <p>Provides APIs for creating and managing SageMaker resources. </p>
  *         <p>Other Resources:</p>
  *         <ul>
  *             <li>
  *                 <p>
- *                   <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html#first-time-user">Amazon SageMaker Developer
+ *                   <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html#first-time-user">SageMaker Developer
  *                         Guide</a>
  *                </p>
  *             </li>

clients/client-sagemaker/src/SageMakerClient.ts

@@ -19,7 +19,7 @@ export interface AddTagsCommandInput extends AddTagsInput {}
 export interface AddTagsCommandOutput extends AddTagsOutput, __MetadataBearer {}
 
 /**
- * <p>Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add
+ * <p>Adds or overwrites one or more tags for the specified SageMaker resource. You can add
  *             tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform
  *             jobs, models, labeling jobs, work teams, endpoint configurations, and
  *             endpoints.</p>

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

@@ -22,7 +22,7 @@ export interface CreateAlgorithmCommandInput extends CreateAlgorithmInput {}
 export interface CreateAlgorithmCommandOutput extends CreateAlgorithmOutput, __MetadataBearer {}
 
 /**
- * <p>Create a machine learning algorithm that you can use in Amazon SageMaker and list in the Amazon Web Services
+ * <p>Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services
  *             Marketplace.</p>
  * @example
  * Use a bare-bones client and the command you need to make an API call.

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

@@ -22,9 +22,9 @@ export interface CreateCodeRepositoryCommandInput extends CreateCodeRepositoryIn
 export interface CreateCodeRepositoryCommandOutput extends CreateCodeRepositoryOutput, __MetadataBearer {}
 
 /**
- * <p>Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the
+ * <p>Creates a Git repository as a resource in your SageMaker account. You can associate the
  *             repository with notebook instances so that you can use Git source control for the
- *             notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can
+ *             notebooks you create. The Git repository is a resource in your SageMaker account, so it can
  *             be associated with more than one notebook instance, and it persists independently from
  *             the lifecycle of any notebook instances it is associated with.</p>
  *         <p>The repository can be hosted either in <a href="https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html">Amazon Web Services CodeCommit</a> or in any

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

@@ -22,11 +22,11 @@ export interface CreateEndpointCommandInput extends CreateEndpointInput {}
 export interface CreateEndpointCommandOutput extends CreateEndpointOutput, __MetadataBearer {}
 
 /**
- * <p>Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker
+ * <p>Creates an endpoint using the endpoint configuration specified in the request. SageMaker
  *             uses the endpoint to provision resources and deploy models. You create the endpoint
  *             configuration with the <a>CreateEndpointConfig</a> API. </p>
- *         <p> Use this API to deploy models using Amazon SageMaker hosting services. </p>
- *         <p>For an example that calls this method when deploying a model to Amazon SageMaker hosting services,
+ *         <p> Use this API to deploy models using SageMaker hosting services. </p>
+ *         <p>For an example that calls this method when deploying a model to SageMaker hosting services,
  *             see the <a href="https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker-fundamentals/create-endpoint/create_endpoint.ipynb">Create Endpoint example notebook.</a>
  *          </p>
  *         <note>
@@ -36,7 +36,7 @@ export interface CreateEndpointCommandOutput extends CreateEndpointOutput, __Met
  *                 create a new <code>EndpointConfig</code>.</p>
  *         </note>
  *         <p>The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account. </p>
- *         <p>When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML
+ *         <p>When it receives the request, SageMaker creates the endpoint, launches the resources (ML
  *             compute instances), and deploys the model(s) on them. </p>
  *
  *         <note>
@@ -51,13 +51,13 @@ export interface CreateEndpointCommandOutput extends CreateEndpointOutput, __Met
  *                 response should return the latest data. So retry logic is recommended to handle
  *                 these possible issues. We also recommend that customers call <a>DescribeEndpointConfig</a> before calling <a>CreateEndpoint</a> to minimize the potential impact of a DynamoDB eventually consistent read.</p>
  *         </note>
- *         <p>When Amazon SageMaker receives the request, it sets the endpoint status to
+ *         <p>When SageMaker receives the request, it sets the endpoint status to
  *                 <code>Creating</code>. After it creates the endpoint, it sets the status to
- *                 <code>InService</code>. Amazon SageMaker can then process incoming requests for inferences. To
+ *                 <code>InService</code>. SageMaker can then process incoming requests for inferences. To
  *             check the status of an endpoint, use the <a>DescribeEndpoint</a>
  *             API.</p>
  *         <p>If any of the models hosted at this endpoint get model data from an Amazon S3 location,
- *             Amazon SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you
+ *             SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you
  *             provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously
  *             deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For
  *             more information, see <a href="https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html">Activating and

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

@@ -22,22 +22,22 @@ export interface CreateEndpointConfigCommandInput extends CreateEndpointConfigIn
 export interface CreateEndpointConfigCommandOutput extends CreateEndpointConfigOutput, __MetadataBearer {}
 
 /**
- * <p>Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In
+ * <p>Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In
  *             the configuration, you identify one or more models, created using the
- *                 <code>CreateModel</code> API, to deploy and the resources that you want Amazon SageMaker to
+ *                 <code>CreateModel</code> API, to deploy and the resources that you want SageMaker to
  *             provision. Then you call the <a>CreateEndpoint</a> API.</p>
  *         <note>
- *             <p> Use this API if you want to use Amazon SageMaker hosting services to deploy models into
+ *             <p> Use this API if you want to use SageMaker hosting services to deploy models into
  *                 production. </p>
  *         </note>
  *         <p>In the request, you define a <code>ProductionVariant</code>, for each model that you
  *             want to deploy. Each <code>ProductionVariant</code> parameter also describes the
- *             resources that you want Amazon SageMaker to provision. This includes the number and type of ML
+ *             resources that you want SageMaker to provision. This includes the number and type of ML
  *             compute instances to deploy. </p>
  *         <p>If you are hosting multiple models, you also assign a <code>VariantWeight</code> to
  *             specify how much traffic you want to allocate to each model. For example, suppose that
  *             you want to host two models, A and B, and you assign traffic weight 2 for model A and 1
- *             for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to
+ *             for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to
  *             model B. </p>
  *         <note>
  *             <p>When you call <a>CreateEndpoint</a>, a load call is made to DynamoDB to

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

@@ -23,7 +23,7 @@ export interface CreateImageCommandOutput extends CreateImageResponse, __Metadat
 
 /**
  * <p>Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image
- *         version represents a container image stored in Amazon Container Registry (ECR). For more information, see
+ *         version represents a container image stored in Amazon Elastic Container Registry (ECR). For more information, see
  *         <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi.html">Bring your own SageMaker image</a>.</p>
  * @example
  * Use a bare-bones client and the command you need to make an API call.

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

@@ -11,7 +11,8 @@ import {
   SerdeContext as __SerdeContext,
 } from "@aws-sdk/types";
 
-import { CreateImageVersionRequest, CreateImageVersionResponse } from "../models/models_0";
+import { CreateImageVersionRequest } from "../models/models_0";
+import { CreateImageVersionResponse } from "../models/models_1";
 import {
   deserializeAws_json1_1CreateImageVersionCommand,
   serializeAws_json1_1CreateImageVersionCommand,
@@ -23,7 +24,7 @@ export interface CreateImageVersionCommandOutput extends CreateImageVersionRespo
 
 /**
  * <p>Creates a version of the SageMaker image specified by <code>ImageName</code>. The version
- *         represents the Amazon Container Registry (ECR) container image specified by <code>BaseImage</code>.</p>
+ *         represents the Amazon Elastic Container Registry (ECR) container image specified by <code>BaseImage</code>.</p>
  * @example
  * Use a bare-bones client and the command you need to make an API call.
  * ```javascript

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

@@ -22,27 +22,25 @@ export interface CreateModelCommandInput extends CreateModelInput {}
 export interface CreateModelCommandOutput extends CreateModelOutput, __MetadataBearer {}
 
 /**
- * <p>Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary
+ * <p>Creates a model in SageMaker. In the request, you name the model and describe a primary
  *             container. For the primary container, you specify the Docker image that
  *             contains inference code, artifacts (from prior training), and a custom environment map
  *             that the inference code uses when you deploy the model for predictions.</p>
- *         <p>Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch
+ *         <p>Use this API to create a model if you want to use SageMaker hosting services or run a batch
  *             transform job.</p>
  *         <p>To host your model, you create an endpoint configuration with the
  *                 <code>CreateEndpointConfig</code> API, and then create an endpoint with the
- *                 <code>CreateEndpoint</code> API. Amazon SageMaker then deploys all of the containers that you
+ *                 <code>CreateEndpoint</code> API. SageMaker then deploys all of the containers that you
  *             defined for the model in the hosting environment. </p>
- *         <p>For an example that calls this method when deploying a model to Amazon SageMaker hosting services,
+ *         <p>For an example that calls this method when deploying a model to SageMaker hosting services,
  *             see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-deploy-model.html#ex1-deploy-model-boto">Deploy the
  *                 Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto
  *             3)).</a>
  *          </p>
  *         <p>To run a batch transform using your model, you start a job with the
- *                 <code>CreateTransformJob</code> API. Amazon SageMaker uses your model and your dataset to get
+ *                 <code>CreateTransformJob</code> API. SageMaker uses your model and your dataset to get
  *             inferences which are then saved to a specified S3 location.</p>
- *         <p>In the <code>CreateModel</code> request, you must define a container with the
- *                 <code>PrimaryContainer</code> parameter.</p>
- *         <p>In the request, you also provide an IAM role that Amazon SageMaker can assume to access model
+ *         <p>In the request, you also provide an IAM role that SageMaker can assume to access model
  *             artifacts and docker image for deployment on ML compute hosting instances or for batch
  *             transform jobs. In addition, you also use the IAM role to manage permissions the
  *             inference code needs. For example, if the inference code access any other Amazon Web Services resources,

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

@@ -22,14 +22,14 @@ export interface CreateModelPackageCommandInput extends CreateModelPackageInput
 export interface CreateModelPackageCommandOutput extends CreateModelPackageOutput, __MetadataBearer {}
 
 /**
- * <p>Creates a model package that you can use to create Amazon SageMaker models or list on Amazon Web Services
+ * <p>Creates a model package that you can use to create SageMaker models or list on Amazon Web Services
  *             Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to
- *             model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker.</p>
+ *             model packages listed on Amazon Web Services Marketplace to create models in SageMaker.</p>
  *         <p>To create a model package by specifying a Docker container that contains your
  *             inference code and the Amazon S3 location of your model artifacts, provide values for
- *                 <code>InferenceSpecification</code>. To create a model from an algorithm resource
+ *             <code>InferenceSpecification</code>. To create a model from an algorithm resource
  *             that you created or subscribed to in Amazon Web Services Marketplace, provide a value for
- *                 <code>SourceAlgorithmSpecification</code>.</p>
+ *             <code>SourceAlgorithmSpecification</code>.</p>
  *         <note>
  *             <p>There are two types of model packages:</p>
  *             <ul>

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

@@ -22,41 +22,41 @@ export interface CreateNotebookInstanceCommandInput extends CreateNotebookInstan
 export interface CreateNotebookInstanceCommandOutput extends CreateNotebookInstanceOutput, __MetadataBearer {}
 
 /**
- * <p>Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML)
+ * <p>Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML)
  *             compute instance running on a Jupyter notebook. </p>
  *         <p>In a <code>CreateNotebookInstance</code> request, specify the type of ML compute
- *             instance that you want to run. Amazon SageMaker launches the instance, installs common libraries
+ *             instance that you want to run. SageMaker launches the instance, installs common libraries
  *             that you can use to explore datasets for model training, and attaches an ML storage
  *             volume to the notebook instance. </p>
- *         <p>Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to
- *             use Amazon SageMaker with a specific algorithm or with a machine learning framework. </p>
- *         <p>After receiving the request, Amazon SageMaker does the following:</p>
+ *         <p>SageMaker also provides a set of example notebooks. Each notebook demonstrates how to
+ *             use SageMaker with a specific algorithm or with a machine learning framework. </p>
+ *         <p>After receiving the request, SageMaker does the following:</p>
  *         <ol>
  *             <li>
- *                 <p>Creates a network interface in the Amazon SageMaker VPC.</p>
+ *                 <p>Creates a network interface in the SageMaker VPC.</p>
  *             </li>
  *             <li>
- *                 <p>(Option) If you specified <code>SubnetId</code>, Amazon SageMaker creates a network
+ *                 <p>(Option) If you specified <code>SubnetId</code>, SageMaker creates a network
  *                     interface in your own VPC, which is inferred from the subnet ID that you provide
- *                     in the input. When creating this network interface, Amazon SageMaker attaches the security
+ *                     in the input. When creating this network interface, SageMaker attaches the security
  *                     group that you specified in the request to the network interface that it creates
  *                     in your VPC.</p>
  *
  *             </li>
  *             <li>
- *                 <p>Launches an EC2 instance of the type specified in the request in the Amazon SageMaker
- *                     VPC. If you specified <code>SubnetId</code> of your VPC, Amazon SageMaker specifies both
+ *                 <p>Launches an EC2 instance of the type specified in the request in the SageMaker
+ *                     VPC. If you specified <code>SubnetId</code> of your VPC, SageMaker specifies both
  *                     network interfaces when launching this instance. This enables inbound traffic
  *                     from your own VPC to the notebook instance, assuming that the security groups
  *                     allow it.</p>
  *             </li>
  *          </ol>
  *
- *         <p>After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).
+ *         <p>After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN).
  *             You can't change the name of a notebook instance after you create it.</p>
- *         <p>After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and
+ *         <p>After SageMaker creates the notebook instance, you can connect to the Jupyter server and
  *             work in Jupyter notebooks. For example, you can write code to explore a dataset that you
- *             can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and
+ *             can use for model training, train a model, host models by creating SageMaker endpoints, and
  *             validate hosted models. </p>
  *         <p>For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html">How It Works</a>. </p>
  * @example