feat(client-sagemaker): SageMaker Inference Recommender now accepts Inference Recommender fields: Domain, Task, Framework, SamplePayloadUrl, SupportedContentTypes, SupportedInstanceTypes, directly in our CreateInferenceRecommendationsJob API through ContainerConfig

Author: awstools

clients/client-sagemaker/src/SageMaker.ts

@@ -2910,7 +2910,7 @@ export class SageMaker extends SageMakerClient {
    *        the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume.
    *        This operation can only be called when the authentication mode equals IAM.
    *    </p>
-   *          <p>The IAM role or user used to call this API defines the permissions to access the app. Once
+   *          <p>The IAM role or user passed to this API defines the permissions to access the app. Once
    *       the presigned URL is created, no additional permission is required to access this URL. IAM
    *          authorization policies for this API are also enforced for every HTTP request and WebSocket
    *          frame that attempts to connect to the app.</p>
@@ -3349,7 +3349,7 @@ export class SageMaker extends SageMakerClient {
    * <p>Creates a user profile. A user profile represents a single user within a domain, and is
    *      the main way to reference a "person" for the purposes of sharing, reporting, and other
    *      user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an
-   *      administrator invites a person by email or imports them from SSO, a user profile is
+   *      administrator invites a person by email or imports them from Amazon Web Services SSO, a user profile is
    *      automatically created. A user profile is the primary holder of settings for an individual
    *      user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
    *    </p>
@@ -3784,7 +3784,7 @@ export class SageMaker extends SageMakerClient {
 
   /**
    * <p>Used to delete a domain.
-   *      If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO.
+   *       If you onboarded with IAM mode, you will need to delete your domain to onboard again using Amazon Web Services SSO.
    *      Use with caution. All of the members of the domain will lose access to their EFS volume,
    *      including data, notebooks, and other artifacts.
    *    </p>

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

@@ -33,7 +33,7 @@ export interface CreatePresignedDomainUrlCommandOutput extends CreatePresignedDo
  *        the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume.
  *        This operation can only be called when the authentication mode equals IAM.
  *    </p>
- *          <p>The IAM role or user used to call this API defines the permissions to access the app. Once
+ *          <p>The IAM role or user passed to this API defines the permissions to access the app. Once
  *       the presigned URL is created, no additional permission is required to access this URL. IAM
  *          authorization policies for this API are also enforced for every HTTP request and WebSocket
  *          frame that attempts to connect to the app.</p>

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

@@ -31,7 +31,7 @@ export interface CreateUserProfileCommandOutput extends CreateUserProfileRespons
  * <p>Creates a user profile. A user profile represents a single user within a domain, and is
  *      the main way to reference a "person" for the purposes of sharing, reporting, and other
  *      user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an
- *      administrator invites a person by email or imports them from SSO, a user profile is
+ *      administrator invites a person by email or imports them from Amazon Web Services SSO, a user profile is
  *      automatically created. A user profile is the primary holder of settings for an individual
  *      user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
  *    </p>

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

@@ -24,7 +24,7 @@ export interface DeleteDomainCommandOutput extends __MetadataBearer {}
 
 /**
  * <p>Used to delete a domain.
- *      If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO.
+ *       If you onboarded with IAM mode, you will need to delete your domain to onboard again using Amazon Web Services SSO.
  *      Use with caution. All of the members of the domain will lose access to their EFS volume,
  *      including data, notebooks, and other artifacts.
  *    </p>

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

@@ -12,12 +12,8 @@ import {
   SerdeContext as __SerdeContext,
 } from "@aws-sdk/types";
 
-import {
-  DescribeFlowDefinitionRequest,
-  DescribeFlowDefinitionRequestFilterSensitiveLog,
-  DescribeFlowDefinitionResponse,
-  DescribeFlowDefinitionResponseFilterSensitiveLog,
-} from "../models/models_1";
+import { DescribeFlowDefinitionRequest, DescribeFlowDefinitionRequestFilterSensitiveLog } from "../models/models_1";
+import { DescribeFlowDefinitionResponse, DescribeFlowDefinitionResponseFilterSensitiveLog } from "../models/models_2";
 import {
   deserializeAws_json1_1DescribeFlowDefinitionCommand,
   serializeAws_json1_1DescribeFlowDefinitionCommand,

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

@@ -12,8 +12,12 @@ import {
   SerdeContext as __SerdeContext,
 } from "@aws-sdk/types";
 
-import { DescribeHumanTaskUiRequest, DescribeHumanTaskUiRequestFilterSensitiveLog } from "../models/models_1";
-import { DescribeHumanTaskUiResponse, DescribeHumanTaskUiResponseFilterSensitiveLog } from "../models/models_2";
+import {
+  DescribeHumanTaskUiRequest,
+  DescribeHumanTaskUiRequestFilterSensitiveLog,
+  DescribeHumanTaskUiResponse,
+  DescribeHumanTaskUiResponseFilterSensitiveLog,
+} from "../models/models_2";
 import {
   deserializeAws_json1_1DescribeHumanTaskUiCommand,
   serializeAws_json1_1DescribeHumanTaskUiCommand,

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

@@ -12,12 +12,8 @@ import {
   SerdeContext as __SerdeContext,
 } from "@aws-sdk/types";
 
-import {
-  ListNotebookInstancesInput,
-  ListNotebookInstancesInputFilterSensitiveLog,
-  ListNotebookInstancesOutput,
-  ListNotebookInstancesOutputFilterSensitiveLog,
-} from "../models/models_2";
+import { ListNotebookInstancesInput, ListNotebookInstancesInputFilterSensitiveLog } from "../models/models_2";
+import { ListNotebookInstancesOutput, ListNotebookInstancesOutputFilterSensitiveLog } from "../models/models_3";
 import {
   deserializeAws_json1_1ListNotebookInstancesCommand,
   serializeAws_json1_1ListNotebookInstancesCommand,

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

@@ -1044,7 +1044,6 @@ export interface OutputDataConfig {
    *                 </p>
    *             </li>
    *          </ul>
-   *
    *         <p>If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must
    *             include permissions to call <code>kms:Encrypt</code>. If you don't provide a KMS key ID,
    *             SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side
@@ -1164,20 +1163,21 @@ export interface ResourceConfig {
    *             algorithms might also use the ML storage volume for scratch space. If you want to store
    *             the training data in the ML storage volume, choose <code>File</code> as the
    *                 <code>TrainingInputMode</code> in the algorithm specification. </p>
-   *         <p>You must specify sufficient ML storage for your scenario. </p>
-   *         <note>
-   *             <p> SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
-   *             </p>
-   *         </note>
-   *         <note>
-   *             <p>Certain Nitro-based instances include local storage with a fixed total size,
-   *                 dependent on the instance type. When using these instances for training, SageMaker mounts
-   *                 the local instance storage instead of Amazon EBS gp2 storage. You can't request a
-   *                     <code>VolumeSizeInGB</code> greater than the total size of the local instance
-   *                 storage.</p>
-   *             <p>For a list of instance types that support local instance storage, including the
-   *                 total size per instance type, see <a href="https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes">Instance Store Volumes</a>.</p>
-   *         </note>
+   *         <p>When using an ML instance with <a href="https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html#nvme-ssd-volumes">NVMe SSD
+   *                 volumes</a>, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage.
+   *             Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures
+   *             storage paths for training datasets, checkpoints, model artifacts, and outputs to use
+   *             the entire capacity of the instance storage. For example, ML instance families with the
+   *             NVMe-type instance storage include <code>ml.p4d</code>, <code>ml.g4dn</code>, and
+   *                 <code>ml.g5</code>. </p>
+   *         <p>When using an ML instance with the EBS-only storage option and without instance
+   *             storage, you must define the size of EBS volume through <code>VolumeSizeInGB</code> in
+   *             the <code>ResourceConfig</code> API. For example, ML instance families that use EBS
+   *             volumes include <code>ml.c5</code> and <code>ml.p2</code>. </p>
+   *         <p>To look up instance types and their instance storage types and volumes, see <a href="http://aws.amazon.com/ec2/instance-types/">Amazon EC2 Instance Types</a>.</p>
+   *         <p>To find the default local paths defined by the SageMaker training platform, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/model-train-storage.html">Amazon SageMaker
+   *                 Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and
+   *                 Outputs</a>.</p>
    */
   VolumeSizeInGB: number | undefined;
 
@@ -1239,8 +1239,8 @@ export interface ResourceConfig {
  */
 export interface StoppingCondition {
   /**
-   * <p>The maximum length of time, in seconds, that a training or compilation job can
-   *             run.</p>
+   * <p>The maximum length of time, in seconds, that a training or compilation job can run
+   *             before it is stopped.</p>
    *         <p>For compilation jobs, if the job does not complete during this time, a
    *                 <code>TimeOut</code> error is generated. We recommend starting with 900 seconds and
    *             increasing as necessary based on your model.</p>
@@ -1249,6 +1249,9 @@ export interface StoppingCondition {
    *                 <code>MaxRuntimeInSeconds</code> specifies the maximum time for all of the attempts
    *             in total, not each individual attempt. The default value is 1 day. The maximum value is
    *             28 days.</p>
+   *         <p>The maximum time that a <code>TrainingJob</code> can run in total, including any time spent
+   *             publishing metrics or archiving and uploading models after it has been stopped, is 30
+   *             days.</p>
    */
   MaxRuntimeInSeconds?: number;
 
@@ -4175,7 +4178,7 @@ export interface AutoMLJobCompletionCriteria {
   MaxCandidates?: number;
 
   /**
-   * <p>The maximum time, in seconds, that each training job is allowed to run as part of a
+   * <p>The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a
    *          hyperparameter tuning job. For more information, see the  used by the  action.</p>
    */
   MaxRuntimePerTrainingJobInSeconds?: number;
@@ -8335,7 +8338,6 @@ export interface ProductionVariantCoreDumpConfig {
    *                 </p>
    *             </li>
    *          </ul>
-   *
    *         <p>If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must
    *             include permissions to call <code>kms:Encrypt</code>. If you don't provide a KMS key ID,
    *             SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side