AWS sagemaker documentation change
Summary
Updated event types list, added HyperPod events, restructured documentation with new examples, and updated EventBridge rule creation references
Security assessment
Changes primarily involve documentation restructuring, adding new event types (HyperPod), and updating examples. No explicit security vulnerabilities or security feature additions are mentioned. The 'SecurityConfig' field in model card examples appears to be a placeholder without security context
Diff
diff --git a/sagemaker/latest/dg/automating-sagemaker-with-eventbridge.md b/sagemaker/latest/dg/automating-sagemaker-with-eventbridge.md index 0dee40410..bac9e2874 100644 --- a//sagemaker/latest/dg/automating-sagemaker-with-eventbridge.md +++ b//sagemaker/latest/dg/automating-sagemaker-with-eventbridge.md @@ -5 +5 @@ -Model state changeTraining job state changeHyperParameter tuning job state changeTransform job state changeEndpoint state changeFeature group state changeModel package state changePipeline execution state changePipeline step state changeProcessing job state changeSageMaker image state changeSageMaker image version state changeEndpoint deployment state changeModel card state change +Endpoint deployment state changeEndpoint state changeFeature group state changeHyperparameter tuning job state changeHyperPod cluster node healthHyperPod cluster state changeImage state changeImage version state changeModel card state changeModel package state changeModel state changePipeline execution state changePipeline step state changeProcessing job state changeTraining job state changeTransform job state change @@ -9 +9 @@ Model state changeTraining job state changeHyperParameter tuning job state chang -Amazon EventBridge monitors status change events in Amazon SageMaker AI. EventBridge enables you to automate SageMaker AI and respond automatically to events such as a training job status change or endpoint status change. Events from SageMaker AI are delivered to EventBridge in near real time. You can write simple rules to indicate which events are of interest to you, and what automated actions to take when an event matches a rule. For an example of how to create a rule, see [Schedule a Pipeline with Amazon EventBridge](./pipeline-eventbridge.html#pipeline-eventbridge-schedule). +Amazon EventBridge monitors status change events in Amazon SageMaker AI. EventBridge enables you to automate SageMaker AI and respond automatically to events such as a training job status change or endpoint status change. Events from SageMaker AI are delivered to EventBridge in near real time. You can write simple rules to indicate which events are of interest to you, and what automated actions to take when an event matches a rule. To create a rule, see [Creating rules that react to events in EventBridge](https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-create-rule.html). If you use AWS CLI, see [put-rule](https://docs.aws.amazon.com/cli/latest/reference/events/put-rule.html) from the _AWS CLI Command Reference_. @@ -34 +34 @@ Some examples of the actions that can be automatically triggered include the fol - * SageMaker AI model state change + * SageMaker endpoint deployment state change @@ -36 +36 @@ Some examples of the actions that can be automatically triggered include the fol - * Training job state change + * SageMaker endpoint state change @@ -38 +38 @@ Some examples of the actions that can be automatically triggered include the fol - * Hyperparameter tuning job state change + * SageMaker feature group state change @@ -40 +40 @@ Some examples of the actions that can be automatically triggered include the fol - * Transform job state change + * SageMaker hyperparameter tuning job state change @@ -42 +42 @@ Some examples of the actions that can be automatically triggered include the fol - * Endpoint state change + * SageMaker HyperPod cluster node health @@ -44 +44 @@ Some examples of the actions that can be automatically triggered include the fol - * Feature group state change + * SageMaker HyperPod cluster state change @@ -46 +46 @@ Some examples of the actions that can be automatically triggered include the fol - * Model package state change + * SageMaker image state change @@ -48 +48 @@ Some examples of the actions that can be automatically triggered include the fol - * Pipeline execution state change + * SageMaker image version state change @@ -50 +50 @@ Some examples of the actions that can be automatically triggered include the fol - * Pipeline step state change + * SageMaker model card state change @@ -52 +52 @@ Some examples of the actions that can be automatically triggered include the fol - * Processing job state change + * SageMaker model package state change @@ -54 +54 @@ Some examples of the actions that can be automatically triggered include the fol - * SageMaker image state change + * SageMaker model state change @@ -56 +56 @@ Some examples of the actions that can be automatically triggered include the fol - * SageMaker image version state change + * SageMaker pipeline execution state change @@ -58 +58 @@ Some examples of the actions that can be automatically triggered include the fol - * Endpoint deployment state change + * SageMaker pipeline step state change @@ -60 +60 @@ Some examples of the actions that can be automatically triggered include the fol - * Model card state change + * SageMaker processing job state change @@ -61,0 +62 @@ Some examples of the actions that can be automatically triggered include the fol + * SageMaker training job state change @@ -62,0 +64 @@ Some examples of the actions that can be automatically triggered include the fol + * SageMaker transform job state change @@ -65 +66,0 @@ Some examples of the actions that can be automatically triggered include the fol -## SageMaker AI model state change @@ -67 +67,0 @@ Some examples of the actions that can be automatically triggered include the fol -Indicates a change in the state of a SageMaker AI model. The state changes when a SageMaker AI model is either created or deleted. @@ -68,0 +69 @@ Indicates a change in the state of a SageMaker AI model. The state changes when +## SageMaker endpoint deployment state change @@ -70,5 +71 @@ Indicates a change in the state of a SageMaker AI model. The state changes when - { - "source": ["aws.sagemaker"], - "detail-type": ["SageMaker Model State Change"] - "Resources" : ["arn:aws:sagemaker:us-east-1:123456789012:model/model-name"] - } +###### Important @@ -76 +73 @@ Indicates a change in the state of a SageMaker AI model. The state changes when -If a model is specified under `Resources`, an event will be generated and sent to EventBridge when the state of this model changes. If you do not specify a value for `Resources`, an event will generate when the status of any of the SageMaker AI models associated with your account changes. +The following examples may not work for all endpoints. For a list of features that may exclude your endpoint, see the [Exclusions](./deployment-guardrails-exclusions.html) page. @@ -78 +75 @@ If a model is specified under `Resources`, an event will be generated and sent t -## Training job state change +Indicates a state change for an endpoint deployment. The following example shows an endpoint updating with a blue/green canary deployment. @@ -80 +76,0 @@ If a model is specified under `Resources`, an event will be generated and sent t -Indicates a change in the status of a SageMaker training job. @@ -82 +78,52 @@ Indicates a change in the status of a SageMaker training job. -If the value of `TrainingJobStatus` is `Failed`, the event contains the `FailureReason` field, which provides a description of why the training job failed. + { + "version": "0", + "id": "0bd4a141-0a02-9d8a-f977-3924c3fb259c", + "detail-type": "SageMaker Endpoint Deployment State Change", + "source": "aws.sagemaker", + "account": "111122223333", + "time": "2021-10-25T01:52:12Z", + "region": "us-west-2", + "resources": [ + "arn:aws:sagemaker:us-west-2:111122223333:endpoint/sample-endpoint" + ], + "detail": { + "EndpointName": "sample-endpoint", + "EndpointArn": "arn:aws:sagemaker:us-west-2:111122223333:endpoint/sample-endpoint", + "EndpointConfigName": "sample-endpoint-config-1", + "ProductionVariants": [ + { + "VariantName": "AllTraffic", + "CurrentWeight": 1, + "DesiredWeight": 1, + "CurrentInstanceCount": 3, + "DesiredInstanceCount": 3 + } + ], + "EndpointStatus": "UPDATING", + "CreationTime": 1635195148181, + "LastModifiedTime": 1635195148181, + "Tags": {}, + "PendingDeploymentSummary": { + "EndpointConfigName": "sample-endpoint-config-2", + "StartTime": Timestamp, + "ProductionVariants": [ + { + "VariantName": "AllTraffic", + "CurrentWeight": 1, + "DesiredWeight": 1, + "CurrentInstanceCount": 1, + "DesiredInstanceCount": 3, + "VariantStatus": [ + { + "Status": "Baking", + "StatusMessage": "Baking for 600 seconds (TerminationWaitInSeconds) with traffic enabled on canary capacity of 1 instance(s).", + "StartTime": 1635195269181, + } + ] + } + ] + } + } + } + +The following example indicates a state change for an endpoint deployment, which is being updated with new capacity on an existing endpoint configuration. @@ -87,2 +134,2 @@ If the value of `TrainingJobStatus` is `Failed`, the event contains the `Failure - "id": "844e2571-85d4-695f-b930-0153b71dcb42", - "detail-type": "SageMaker Training Job State Change", + "id": "0bd4a141-0a02-9d8a-f977-3924c3fb259c", + "detail-type": "SageMaker Endpoint Deployment State Change", @@ -90,3 +137,3 @@ If the value of `TrainingJobStatus` is `Failed`, the event contains the `Failure - "account": "123456789012", - "time": "2018-10-06T12:26:13Z", - "region": "us-east-1", + "account": "111122223333", + "time": "2021-10-25T01:52:12Z", + "region": "us-west-2", @@ -94 +141 @@ If the value of `TrainingJobStatus` is `Failed`, the event contains the `Failure - "arn:aws:sagemaker:us-east-1:123456789012:training-job/kmeans-1" + "arn:aws:sagemaker:us-west-2:651393343886:endpoint/sample-endpoint" @@ -97,13 +144,4 @@ If the value of `TrainingJobStatus` is `Failed`, the event contains the `Failure - "TrainingJobName": "89c96cc8-dded-4739-afcc-6f1dc936701d", - "TrainingJobArn": "arn:aws:sagemaker:us-east-1:123456789012:training-job/kmeans-1", - "TrainingJobStatus": "Completed", - "SecondaryStatus": "Completed", - "HyperParameters": { - "Hyper": "Parameters" - }, - "AlgorithmSpecification": { - "TrainingImage": "TrainingImage", - "TrainingInputMode": "TrainingInputMode" - }, - "RoleArn": "arn:aws:iam::123456789012:role/SMRole", - "InputDataConfig": [ + "EndpointName": "sample-endpoint", + "EndpointArn": "arn:aws:sagemaker:us-west-2:651393343886:endpoint/sample-endpoint", + "EndpointConfigName": "sample-endpoint-config-1", + "ProductionVariants": [ @@ -111,6 +149,10 @@ If the value of `TrainingJobStatus` is `Failed`, the event contains the `Failure - "ChannelName": "Train", - "DataSource": { - "S3DataSource": { - "S3DataType": "S3DataType", - "S3Uri": "S3Uri", - "S3DataDistributionType": "S3DataDistributionType" + "VariantName": "AllTraffic", + "CurrentWeight": 1, + "DesiredWeight": 1, + "CurrentInstanceCount": 3, + "DesiredInstanceCount": 6, + "VariantStatus": [ + { + "Status": "Updating", + "StatusMessage": "Scaling out desired instance count to 6.", + "StartTime": 1635195269181, @@ -118,4 +160 @@ If the value of `TrainingJobStatus` is `Failed`, the event contains the `Failure - }, - "ContentType": "ContentType", - "CompressionType": "CompressionType", - "RecordWrapperType": "RecordWrapperType" + ] @@ -124,11 +163,36 @@ If the value of `TrainingJobStatus` is `Failed`, the event contains the `Failure - "OutputDataConfig": { - "KmsKeyId": "KmsKeyId", - "S3OutputPath": "S3OutputPath" - }, - "ResourceConfig": { - "InstanceType": "InstanceType", - "InstanceCount": 3, - "VolumeSizeInGB": 20, - "VolumeKmsKeyId": "VolumeKmsKeyId" - }, - "VpcConfig": { + "EndpointStatus": "UPDATING",