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feat(client-sagemaker): This release adds Mode to AutoMLJobConfig.
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-41
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‎clients/client-sagemaker/src/SageMaker.ts‎

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

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

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

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

Lines changed: 6 additions & 2 deletions
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@@ -12,8 +12,12 @@ import {
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SerdeContext as __SerdeContext,
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} from "@aws-sdk/types";
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import { DescribeFlowDefinitionRequest, DescribeFlowDefinitionRequestFilterSensitiveLog } from "../models/models_1";
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import { DescribeFlowDefinitionResponse, DescribeFlowDefinitionResponseFilterSensitiveLog } from "../models/models_2";
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import {
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DescribeFlowDefinitionRequest,
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DescribeFlowDefinitionRequestFilterSensitiveLog,
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DescribeFlowDefinitionResponse,
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DescribeFlowDefinitionResponseFilterSensitiveLog,
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} from "../models/models_2";
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import {
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deserializeAws_json1_1DescribeFlowDefinitionCommand,
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serializeAws_json1_1DescribeFlowDefinitionCommand,

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

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@@ -4059,7 +4059,8 @@ export interface AutoMLS3DataSource {
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* <p>The data type.</p>
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* <p>A ManifestFile should have the format shown below:</p>
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* <p>
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* <code>[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, </code>
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* <code>[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},
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* </code>
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* </p>
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* <p>
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* <code>"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",</code>
@@ -4178,8 +4179,9 @@ export interface AutoMLJobCompletionCriteria {
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MaxCandidates?: number;
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/**
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* <p>The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a
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* hyperparameter tuning job. For more information, see the used by the action.</p>
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* <p>The maximum time, in seconds, that each training job executed inside hyperparameter
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* tuning is allowed to run as part of a hyperparameter tuning job. For more information, see
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* the used by the action.</p>
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*/
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MaxRuntimePerTrainingJobInSeconds?: number;
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@@ -4193,6 +4195,12 @@ export interface AutoMLJobCompletionCriteria {
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MaxAutoMLJobRuntimeInSeconds?: number;
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}
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export enum AutoMLMode {
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AUTO = "AUTO",
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ENSEMBLING = "ENSEMBLING",
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HYPERPARAMETER_TUNING = "HYPERPARAMETER_TUNING",
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}
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/**
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* <p>Specifies a VPC that your training jobs and hosted models have access to. Control
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* access to and from your training and model containers by configuring the VPC. For more
@@ -4259,6 +4267,26 @@ export interface AutoMLJobConfig {
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* <p>The configuration for generating a candidate for an AutoML job (optional). </p>
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*/
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CandidateGenerationConfig?: AutoMLCandidateGenerationConfig;
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/**
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* <p>The method that Autopilot uses to train the data. You can either specify the mode manually
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* or let Autopilot choose for you based on the dataset size by selecting <code>AUTO</code>. In
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* <code>AUTO</code> mode, Autopilot chooses <code>ENSEMBLING</code> for datasets smaller than
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* 100 MB, and <code>HYPERPARAMETER_TUNING</code> for larger ones.</p>
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* <p>The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict
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* classification and regression tasks directly from your dataset. This machine learning mode
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* combines several base models to produce an optimal predictive model. It then uses a
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* stacking ensemble method to combine predictions from contributing members. A multi-stack
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* ensemble model can provide better performance over a single model by combining the
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* 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
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* <code>ENSEMBLING</code> mode.</p>
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* <p>The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train
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* the best version of a model. HPO will automatically select an algorithm for the type of
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* problem you want to solve. Then HPO finds the best hyperparameters according to your
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* 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
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* <code>HYPERPARAMETER_TUNING</code> mode.</p>
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*/
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Mode?: AutoMLMode | string;
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}
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42644292
/**
@@ -8445,15 +8473,15 @@ export interface ProductionVariant {
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VolumeSizeInGB?: number;
84468474

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/**
8448-
* <p>The timeout value, in seconds, to download and extract customer
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* model artifact from Amazon S3 to individual inference instance associated with
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* <p>The timeout value, in seconds, to download and extract the
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* model that you want to host from Amazon S3 to the individual inference instance associated with
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* this production variant.</p>
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*/
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ModelDataDownloadTimeoutInSeconds?: number;
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/**
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* <p>The timeout value, in seconds, for the customer inference container
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* to pass health check by SageMaker Hosting. For more information on health
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* <p>The timeout value, in seconds, for your inference container
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* to pass health check by SageMaker Hosting. For more information about health
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* check, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests">How
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* Your Container Should Respond to Health Check (Ping)
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* Requests</a>.</p>
@@ -9861,11 +9889,6 @@ export enum HyperParameterTuningJobStrategyType {
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RANDOM = "Random",
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}
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export enum TrainingJobEarlyStoppingType {
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AUTO = "Auto",
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OFF = "Off",
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}
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/**
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* @internal
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*/

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

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@@ -96,7 +96,6 @@ import {
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Tag,
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TrainingInputMode,
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TrainingInstanceType,
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TrainingJobEarlyStoppingType,
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TrainingSpecification,
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TransformInput,
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TransformJobDefinition,
@@ -107,6 +106,11 @@ import {
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VpcConfig,
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} from "./models_0";
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export enum TrainingJobEarlyStoppingType {
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AUTO = "Auto",
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OFF = "Off",
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}
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/**
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* <p>The job completion criteria.</p>
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*/
@@ -5656,6 +5660,12 @@ export interface CreateTrainingJobRequest {
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* <p>You can specify a maximum of 100 hyperparameters. Each hyperparameter is a
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* key-value pair. Each key and value is limited to 256 characters, as specified by the
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* <code>Length Constraint</code>. </p>
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* <important>
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* <p>You must not include any security-sensitive information, such as
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* account access IDs, secrets, and tokens, in the dictionary for configuring
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* hyperparameters. SageMaker rejects the training job request and returns an
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* exception error for detected credentials, if such user input is found.</p>
5668+
* </important>
56595669
*/
56605670
HyperParameters?: Record<string, string>;
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@@ -9163,13 +9173,6 @@ export interface DescribeFeatureMetadataResponse {
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Parameters?: FeatureParameter[];
91649174
}
91659175

9166-
export interface DescribeFlowDefinitionRequest {
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/**
9168-
* <p>The name of the flow definition.</p>
9169-
*/
9170-
FlowDefinitionName: string | undefined;
9171-
}
9172-
91739176
/**
91749177
* @internal
91759178
*/
@@ -11118,10 +11121,3 @@ export const FeatureParameterFilterSensitiveLog = (obj: FeatureParameter): any =
1111811121
export const DescribeFeatureMetadataResponseFilterSensitiveLog = (obj: DescribeFeatureMetadataResponse): any => ({
1111911122
...obj,
1112011123
});
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/**
11123-
* @internal
11124-
*/
11125-
export const DescribeFlowDefinitionRequestFilterSensitiveLog = (obj: DescribeFlowDefinitionRequest): any => ({
11126-
...obj,
11127-
});

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

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@@ -142,6 +142,13 @@ import {
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TrialComponentStatus,
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} from "./models_1";
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145+
export interface DescribeFlowDefinitionRequest {
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/**
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* <p>The name of the flow definition.</p>
148+
*/
149+
FlowDefinitionName: string | undefined;
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}
151+
145152
export enum FlowDefinitionStatus {
146153
ACTIVE = "Active",
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DELETING = "Deleting",
@@ -8833,10 +8840,12 @@ export enum NotebookInstanceSortKey {
88338840
STATUS = "Status",
88348841
}
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8836-
export enum NotebookInstanceSortOrder {
8837-
ASCENDING = "Ascending",
8838-
DESCENDING = "Descending",
8839-
}
8843+
/**
8844+
* @internal
8845+
*/
8846+
export const DescribeFlowDefinitionRequestFilterSensitiveLog = (obj: DescribeFlowDefinitionRequest): any => ({
8847+
...obj,
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});
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/**
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* @internal

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

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@@ -99,7 +99,6 @@ import {
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ModelPackageGroupStatus,
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ModelPackageStatusDetails,
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NotebookInstanceSortKey,
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NotebookInstanceSortOrder,
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NotebookInstanceStatus,
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PipelineExecutionStatus,
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PipelineExperimentConfig,
@@ -123,6 +122,11 @@ import {
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Workteam,
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} from "./models_2";
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125+
export enum NotebookInstanceSortOrder {
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ASCENDING = "Ascending",
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DESCENDING = "Descending",
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}
129+
126130
export interface ListNotebookInstancesInput {
127131
/**
128132
* <p> If the previous call to the <code>ListNotebookInstances</code> is truncated, the

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

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Original file line numberDiff line numberDiff line change
@@ -1076,7 +1076,6 @@ import {
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DescribeFeatureGroupResponse,
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DescribeFeatureMetadataRequest,
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DescribeFeatureMetadataResponse,
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DescribeFlowDefinitionRequest,
10801079
DriftCheckBaselines,
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DriftCheckBias,
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DriftCheckExplainability,
@@ -1190,6 +1189,7 @@ import {
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WorkforceVpcConfigRequest,
11911190
} from "../models/models_1";
11921191
import {
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DescribeFlowDefinitionRequest,
11931193
DescribeFlowDefinitionResponse,
11941194
DescribeHumanTaskUiRequest,
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DescribeHumanTaskUiResponse,
@@ -15663,6 +15663,7 @@ const serializeAws_json1_1AutoMLJobConfig = (input: AutoMLJobConfig, context: __
1566315663
...(input.DataSplitConfig != null && {
1566415664
DataSplitConfig: serializeAws_json1_1AutoMLDataSplitConfig(input.DataSplitConfig, context),
1566515665
}),
15666+
...(input.Mode != null && { Mode: input.Mode }),
1566615667
...(input.SecurityConfig != null && {
1566715668
SecurityConfig: serializeAws_json1_1AutoMLSecurityConfig(input.SecurityConfig, context),
1566815669
}),
@@ -23517,6 +23518,7 @@ const deserializeAws_json1_1AutoMLJobConfig = (output: any, context: __SerdeCont
2351723518
output.DataSplitConfig != null
2351823519
? deserializeAws_json1_1AutoMLDataSplitConfig(output.DataSplitConfig, context)
2351923520
: undefined,
23521+
Mode: __expectString(output.Mode),
2352023522
SecurityConfig:
2352123523
output.SecurityConfig != null
2352223524
? deserializeAws_json1_1AutoMLSecurityConfig(output.SecurityConfig, context)

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