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Amazon SageMaker Service Update: This release adds a new API UpdateClusterSoftware for SageMaker HyperPod. This API allows users to patch HyperPod clusters with latest platform softwares.
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{
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"type": "feature",
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"category": "Amazon SageMaker Service",
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"contributor": "",
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"description": "This release adds a new API UpdateClusterSoftware for SageMaker HyperPod. This API allows users to patch HyperPod clusters with latest platform softwares."
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}

services/sagemaker/src/main/resources/codegen-resources/service-2.json

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{"shape":"ResourceNotFound"},
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{"shape":"ConflictException"}
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],
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"documentation":"<p>Update a SageMaker HyperPod cluster.</p>"
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"documentation":"<p>Updates a SageMaker HyperPod cluster.</p>"
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},
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"UpdateClusterSoftware":{
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"name":"UpdateClusterSoftware",
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"http":{
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"method":"POST",
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"requestUri":"/"
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},
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"input":{"shape":"UpdateClusterSoftwareRequest"},
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"output":{"shape":"UpdateClusterSoftwareResponse"},
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"errors":[
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{"shape":"ResourceNotFound"},
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{"shape":"ConflictException"}
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],
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"documentation":"<p>Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod-operate.html#sagemaker-hyperpod-operate-cli-command-update-cluster-software\">Update the SageMaker HyperPod platform software of a cluster</a>.</p>"
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},
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"UpdateCodeRepository":{
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"name":"UpdateCodeRepository",
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"documentation":"<p>The path of the S3 object that contains the model artifacts. For example, <code>s3://bucket-name/keynameprefix/model.tar.gz</code>.</p>"
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}
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},
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"documentation":"<p>Provides information about the location that is configured for storing model artifacts. </p> <p>Model artifacts are the output that results from training a model, and typically consist of trained parameters, a model definition that describes how to compute inferences, and other metadata.</p>"
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"documentation":"<p>Provides information about the location that is configured for storing model artifacts. </p> <p>Model artifacts are outputs that result from training a model. They typically consist of trained parameters, a model definition that describes how to compute inferences, and other metadata. A SageMaker container stores your trained model artifacts in the <code>/opt/ml/model</code> directory. After training has completed, by default, these artifacts are uploaded to your Amazon S3 bucket as compressed files.</p>"
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},
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"ModelBiasAppSpecification":{
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"type":"structure",
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}
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}
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},
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"UpdateClusterSoftwareRequest":{
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"type":"structure",
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"required":["ClusterName"],
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"members":{
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"ClusterName":{
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"shape":"ClusterNameOrArn",
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"documentation":"<p>Specify the name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster you want to update for security patching.</p>"
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}
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}
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},
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"UpdateClusterSoftwareResponse":{
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"type":"structure",
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"required":["ClusterArn"],
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"members":{
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"ClusterArn":{
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"shape":"ClusterArn",
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"documentation":"<p>The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster being updated for security patching.</p>"
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}
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}
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},
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"UpdateCodeRepositoryInput":{
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"type":"structure",
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"required":["CodeRepositoryName"],

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