AWS sagemaker documentation change
Summary
Added deprecation notice for SageMaker Debugger and removed legacy SDK v2 code examples
Security assessment
The change announces service deprecation without addressing any specific vulnerability. The mention of security improvements is generic and lacks evidence of addressing a concrete security issue.
Diff
diff --git a/sagemaker/latest/dg/debugger-configure-tensor-collections.md b/sagemaker/latest/dg/debugger-configure-tensor-collections.md index cd88fcf18..1537ea851 100644 --- a//sagemaker/latest/dg/debugger-configure-tensor-collections.md +++ b//sagemaker/latest/dg/debugger-configure-tensor-collections.md @@ -9 +9 @@ -Use the `CollectionConfig` API operation to configure tensor collections. Debugger provides pre-built tensor collections that cover a variety of regular expressions (regex) of parameters if using Debugger-supported deep learning frameworks and machine learning algorithms. As shown in the following example code, add the built-in tensor collections you want to debug. +###### Note @@ -11 +11 @@ Use the `CollectionConfig` API operation to configure tensor collections. Debugg -SageMaker Python SDK v3 +After careful consideration, we have made the decision to close new customer access to Amazon Sagemaker Debugger, effective 6/30/26. Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Debugger, but we do not plan to introduce new features. For more information, see [Debugger availability change](./debugger-availability-change.html). @@ -12,0 +13 @@ SageMaker Python SDK v3 +Use the `CollectionConfig` API operation to configure tensor collections. Debugger provides pre-built tensor collections that cover a variety of regular expressions (regex) of parameters if using Debugger-supported deep learning frameworks and machine learning algorithms. As shown in the following example code, add the built-in tensor collections you want to debug. @@ -22,11 +22,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - from sagemaker.debugger import CollectionConfig - - collection_configs=[ - CollectionConfig(name="weights"), - CollectionConfig(name="gradients") - ] - @@ -39,3 +28,0 @@ If you want to customize the built-in collections, such as changing the save int -SageMaker Python SDK v3 - - @@ -57,18 +43,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - from sagemaker.debugger import CollectionConfig - - collection_configs=[ - CollectionConfig( - name="tensor_collection", - parameters={ - "key_1": "value_1", - "key_2": "value_2", - ... - "key_n": "value_n" - } - ) - ] - @@ -77,3 +45,0 @@ For more information about available parameter keys, see [CollectionConfig](http -SageMaker Python SDK v3 - - @@ -93,16 +58,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - from sagemaker.debugger import CollectionConfig - - collection_configs=[ - CollectionConfig( - name="losses", - parameters={ - "train.save_interval": "100", - "eval.save_interval": "10" - } - ) - ] -