AWS sagemaker documentation change
Summary
Removed legacy SageMaker Python SDK v2 code examples for model deployment and resource configuration.
Security assessment
Removal of outdated SDK examples doesn't indicate security fixes. No security context or vulnerability references present in the change.
Diff
diff --git a/sagemaker/latest/dg/how-it-works-modelbuilder-creation.md b/sagemaker/latest/dg/how-it-works-modelbuilder-creation.md index 25a74b847..06f1fee3d 100644 --- a//sagemaker/latest/dg/how-it-works-modelbuilder-creation.md +++ b//sagemaker/latest/dg/how-it-works-modelbuilder-creation.md @@ -210,3 +209,0 @@ Deploy your model with the model’s existing `deploy` method. In this step, Sag -SageMaker Python SDK v3 - - @@ -219,9 +215,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - predictor = model.deploy( - initial_instance_count=1, - instance_type="ml.c6i.xlarge" - ) - @@ -230,3 +217,0 @@ If you want more fine-grained control over the endpoint resources assigned to yo -SageMaker Python SDK v3 - - @@ -250,19 +234,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - resource_requirements = ResourceRequirements( - requests={ - "num_accelerators": 4, - "memory": 1024, - "copies": 1, - }, - limits={}, - ) - predictor = model.deploy( - mode=Mode.SAGEMAKER_ENDPOINT, - endpoint_type=EndpointType.INFERENCE_COMPONENT_BASED, - resources=resource_requirements, - role="role" - ) - @@ -303,3 +268,0 @@ Call the `deploy` function to deploy locally, as shown in the following snippet. -SageMaker Python SDK v3 - - @@ -309,6 +271,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - predictor_local = xgb_local_builder.deploy() -