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1634 | 1634 | "ChannelSpecification$IsRequired": "<p>Indicates whether the channel is required by the algorithm.</p>",
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1635 | 1635 | "ClarifyCheckStepMetadata$SkipCheck": "<p>This flag indicates if the drift check against the previous baseline will be skipped or not. If it is set to <code>False</code>, the previous baseline of the configured check type must be available.</p>",
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1636 | 1636 | "ClarifyCheckStepMetadata$RegisterNewBaseline": "<p>This flag indicates if a newly calculated baseline can be accessed through step properties <code>BaselineUsedForDriftCheckConstraints</code> and <code>BaselineUsedForDriftCheckStatistics</code>. If it is set to <code>False</code>, the previous baseline of the configured check type must also be available. These can be accessed through the <code>BaselineUsedForDriftCheckConstraints</code> property. </p>",
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| 1637 | + "CreateAppRequest$RecoveryMode": "<p> Indicates whether the application is launched in recovery mode. </p>", |
1637 | 1638 | "CreateEndpointConfigInput$EnableNetworkIsolation": "<p>Sets whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.</p>",
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1638 | 1639 | "CreateMlflowTrackingServerRequest$AutomaticModelRegistration": "<p>Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to <code>True</code>. To disable automatic model registration, set this value to <code>False</code>. If not specified, <code>AutomaticModelRegistration</code> defaults to <code>False</code>.</p>",
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1639 | 1640 | "CreateModelInput$EnableNetworkIsolation": "<p>Isolates the model container. No inbound or outbound network calls can be made to or from the model container.</p>",
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1640 | 1641 | "CreatePartnerAppRequest$EnableIamSessionBasedIdentity": "<p>When set to <code>TRUE</code>, the SageMaker Partner AI App sets the Amazon Web Services IAM session name or the authenticated IAM user as the identity of the SageMaker Partner AI App user.</p>",
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1641 | 1642 | "CreateTrainingJobRequest$EnableNetworkIsolation": "<p>Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.</p>",
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1642 | 1643 | "CreateTrainingJobRequest$EnableInterContainerTrafficEncryption": "<p>To encrypt all communications between ML compute instances in distributed training, choose <code>True</code>. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/train-encrypt.html\">Protect Communications Between ML Compute Instances in a Distributed Training Job</a>.</p>",
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1643 | 1644 | "CreateTrainingJobRequest$EnableManagedSpotTraining": "<p>To train models using managed spot training, choose <code>True</code>. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run. </p> <p>The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed. </p>",
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| 1645 | + "DescribeAppResponse$RecoveryMode": "<p> Indicates whether the application is launched in recovery mode. </p>", |
1644 | 1646 | "DescribeEndpointConfigOutput$EnableNetworkIsolation": "<p>Indicates whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.</p>",
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1645 | 1647 | "DescribeMlflowTrackingServerResponse$AutomaticModelRegistration": "<p>Whether automatic registration of new MLflow models to the SageMaker Model Registry is enabled.</p>",
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1646 | 1648 | "DescribeModelOutput$EnableNetworkIsolation": "<p>If <code>True</code>, no inbound or outbound network calls can be made to or from the model container.</p>",
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