feat(client-sagemaker): This release adds Mode to AutoMLJobConfig.

Author: awstools

clients/client-sagemaker/src/SageMaker.ts

@@ -3120,6 +3120,12 @@ export class SageMaker extends SageMakerClient {
    *                     enable the estimation of model parameters during training. Hyperparameters can
    *                     be tuned to optimize this learning process. For a list of hyperparameters for
    *                     each training algorithm provided by SageMaker, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html">Algorithms</a>. </p>
+   *                 <important>
+   *                   <p>You must not include any security-sensitive information, such as
+   *                     account access IDs, secrets, and tokens, in the dictionary for configuring
+   *                     hyperparameters. SageMaker rejects the training job request and returns an
+   *                     exception error for detected credentials, if such user input is found.</p>
+   *                </important>
    *             </li>
    *             <li>
    *                 <p>

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

@@ -49,6 +49,12 @@ export interface CreateTrainingJobCommandOutput extends CreateTrainingJobRespons
  *                     enable the estimation of model parameters during training. Hyperparameters can
  *                     be tuned to optimize this learning process. For a list of hyperparameters for
  *                     each training algorithm provided by SageMaker, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html">Algorithms</a>. </p>
+ *                 <important>
+ *                   <p>You must not include any security-sensitive information, such as
+ *                     account access IDs, secrets, and tokens, in the dictionary for configuring
+ *                     hyperparameters. SageMaker rejects the training job request and returns an
+ *                     exception error for detected credentials, if such user input is found.</p>
+ *                </important>
  *             </li>
  *             <li>
  *                 <p>

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

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

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

@@ -4059,7 +4059,8 @@ export interface AutoMLS3DataSource {
    * <p>The data type.</p>
    *          <p>A ManifestFile should have the format shown below:</p>
    *          <p>
-   *             <code>[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, </code>
+   *             <code>[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},
+   *          </code>
    *          </p>
    *          <p>
    *             <code>"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",</code>
@@ -4178,8 +4179,9 @@ export interface AutoMLJobCompletionCriteria {
   MaxCandidates?: number;
 
   /**
-   * <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>
+   * <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;
 
@@ -4193,6 +4195,12 @@ export interface AutoMLJobCompletionCriteria {
   MaxAutoMLJobRuntimeInSeconds?: number;
 }
 
+export enum AutoMLMode {
+  AUTO = "AUTO",
+  ENSEMBLING = "ENSEMBLING",
+  HYPERPARAMETER_TUNING = "HYPERPARAMETER_TUNING",
+}
+
 /**
  * <p>Specifies a VPC that your training jobs and hosted models have access to. Control
  *             access to and from your training and model containers by configuring the VPC. For more
@@ -4259,6 +4267,26 @@ export interface AutoMLJobConfig {
    * <p>The configuration for generating a candidate for an AutoML job (optional). </p>
    */
   CandidateGenerationConfig?: AutoMLCandidateGenerationConfig;
+
+  /**
+   * <p>The method that Autopilot uses to train the data. You can either specify the mode manually
+   *          or let Autopilot choose for you based on the dataset size by selecting <code>AUTO</code>. In
+   *             <code>AUTO</code> mode, Autopilot chooses <code>ENSEMBLING</code> for datasets smaller than
+   *          100 MB, and <code>HYPERPARAMETER_TUNING</code> for larger ones.</p>
+   *          <p>The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict
+   *          classification and regression tasks directly from your dataset. This machine learning mode
+   *          combines several base models to produce an optimal predictive model. It then uses a
+   *          stacking ensemble method to combine predictions from contributing members. A multi-stack
+   *          ensemble model can provide better performance over a single model by combining the
+   *          predictive capabilities of multiple models. See <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-suppprt">Autopilot algorithm support</a> for a list of algorithms supported by
+   *             <code>ENSEMBLING</code> mode.</p>
+   *          <p>The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train
+   *          the best version of a model. HPO will automatically select an algorithm for the type of
+   *          problem you want to solve. Then HPO finds the best hyperparameters according to your
+   *          objective metric. See <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-suppprt">Autopilot algorithm support</a> for a list of algorithms supported by
+   *             <code>HYPERPARAMETER_TUNING</code> mode.</p>
+   */
+  Mode?: AutoMLMode | string;
 }
 
 /**
@@ -8445,15 +8473,15 @@ export interface ProductionVariant {
   VolumeSizeInGB?: number;
 
   /**
-   * <p>The timeout value, in seconds, to download and extract customer
-   *           model artifact from Amazon S3 to individual inference instance associated with
+   * <p>The timeout value, in seconds, to download and extract the
+   *           model that you want to host from Amazon S3 to the individual inference instance associated with
    *           this production variant.</p>
    */
   ModelDataDownloadTimeoutInSeconds?: number;
 
   /**
-   * <p>The timeout value, in seconds, for the customer inference container
-   *           to pass health check by SageMaker Hosting. For more information on health
+   * <p>The timeout value, in seconds, for your inference container
+   *           to pass health check by SageMaker Hosting. For more information about health
    *           check, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests">How
    *           Your Container Should Respond to Health Check (Ping)
    *           Requests</a>.</p>
@@ -9861,11 +9889,6 @@ export enum HyperParameterTuningJobStrategyType {
   RANDOM = "Random",
 }
 
-export enum TrainingJobEarlyStoppingType {
-  AUTO = "Auto",
-  OFF = "Off",
-}
-
 /**
  * @internal
  */

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

@@ -96,7 +96,6 @@ import {
   Tag,
   TrainingInputMode,
   TrainingInstanceType,
-  TrainingJobEarlyStoppingType,
   TrainingSpecification,
   TransformInput,
   TransformJobDefinition,
@@ -107,6 +106,11 @@ import {
   VpcConfig,
 } from "./models_0";
 
+export enum TrainingJobEarlyStoppingType {
+  AUTO = "Auto",
+  OFF = "Off",
+}
+
 /**
  * <p>The job completion criteria.</p>
  */
@@ -5656,6 +5660,12 @@ export interface CreateTrainingJobRequest {
    *         <p>You can specify a maximum of 100 hyperparameters. Each hyperparameter is a
    *             key-value pair. Each key and value is limited to 256 characters, as specified by the
    *                 <code>Length Constraint</code>. </p>
+   *         <important>
+   *             <p>You must not include any security-sensitive information, such as
+   *             account access IDs, secrets, and tokens, in the dictionary for configuring
+   *             hyperparameters. SageMaker rejects the training job request and returns an
+   *             exception error for detected credentials, if such user input is found.</p>
+   *          </important>
    */
   HyperParameters?: Record<string, string>;
 
@@ -9163,13 +9173,6 @@ export interface DescribeFeatureMetadataResponse {
   Parameters?: FeatureParameter[];
 }
 
-export interface DescribeFlowDefinitionRequest {
-  /**
-   * <p>The name of the flow definition.</p>
-   */
-  FlowDefinitionName: string | undefined;
-}
-
 /**
  * @internal
  */
@@ -11118,10 +11121,3 @@ export const FeatureParameterFilterSensitiveLog = (obj: FeatureParameter): any =
 export const DescribeFeatureMetadataResponseFilterSensitiveLog = (obj: DescribeFeatureMetadataResponse): any => ({
   ...obj,
 });
-
-/**
- * @internal
- */
-export const DescribeFlowDefinitionRequestFilterSensitiveLog = (obj: DescribeFlowDefinitionRequest): any => ({
-  ...obj,
-});

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

@@ -142,6 +142,13 @@ import {
   TrialComponentStatus,
 } from "./models_1";
 
+export interface DescribeFlowDefinitionRequest {
+  /**
+   * <p>The name of the flow definition.</p>
+   */
+  FlowDefinitionName: string | undefined;
+}
+
 export enum FlowDefinitionStatus {
   ACTIVE = "Active",
   DELETING = "Deleting",
@@ -8833,10 +8840,12 @@ export enum NotebookInstanceSortKey {
   STATUS = "Status",
 }
 
-export enum NotebookInstanceSortOrder {
-  ASCENDING = "Ascending",
-  DESCENDING = "Descending",
-}
+/**
+ * @internal
+ */
+export const DescribeFlowDefinitionRequestFilterSensitiveLog = (obj: DescribeFlowDefinitionRequest): any => ({
+  ...obj,
+});
 
 /**
  * @internal

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

@@ -99,7 +99,6 @@ import {
   ModelPackageGroupStatus,
   ModelPackageStatusDetails,
   NotebookInstanceSortKey,
-  NotebookInstanceSortOrder,
   NotebookInstanceStatus,
   PipelineExecutionStatus,
   PipelineExperimentConfig,
@@ -123,6 +122,11 @@ import {
   Workteam,
 } from "./models_2";
 
+export enum NotebookInstanceSortOrder {
+  ASCENDING = "Ascending",
+  DESCENDING = "Descending",
+}
+
 export interface ListNotebookInstancesInput {
   /**
    * <p> If the previous call to the <code>ListNotebookInstances</code> is truncated, the

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

@@ -1076,7 +1076,6 @@ import {
   DescribeFeatureGroupResponse,
   DescribeFeatureMetadataRequest,
   DescribeFeatureMetadataResponse,
-  DescribeFlowDefinitionRequest,
   DriftCheckBaselines,
   DriftCheckBias,
   DriftCheckExplainability,
@@ -1190,6 +1189,7 @@ import {
   WorkforceVpcConfigRequest,
 } from "../models/models_1";
 import {
+  DescribeFlowDefinitionRequest,
   DescribeFlowDefinitionResponse,
   DescribeHumanTaskUiRequest,
   DescribeHumanTaskUiResponse,
@@ -15663,6 +15663,7 @@ const serializeAws_json1_1AutoMLJobConfig = (input: AutoMLJobConfig, context: __
     ...(input.DataSplitConfig != null && {
       DataSplitConfig: serializeAws_json1_1AutoMLDataSplitConfig(input.DataSplitConfig, context),
     }),
+    ...(input.Mode != null && { Mode: input.Mode }),
     ...(input.SecurityConfig != null && {
       SecurityConfig: serializeAws_json1_1AutoMLSecurityConfig(input.SecurityConfig, context),
     }),
@@ -23517,6 +23518,7 @@ const deserializeAws_json1_1AutoMLJobConfig = (output: any, context: __SerdeCont
       output.DataSplitConfig != null
         ? deserializeAws_json1_1AutoMLDataSplitConfig(output.DataSplitConfig, context)
         : undefined,
+    Mode: __expectString(output.Mode),
     SecurityConfig:
       output.SecurityConfig != null
         ? deserializeAws_json1_1AutoMLSecurityConfig(output.SecurityConfig, context)