AWS sagemaker documentation change

Service: sagemaker · 2026-07-01 · Documentation low

File: sagemaker/latest/dg/debugger-built-in-rules-example.md

Summary

Added deprecation notice for Amazon SageMaker Debugger and removed all SageMaker Python SDK v2 legacy code examples

Security assessment

The change only communicates service availability changes and removes outdated code samples. No security vulnerabilities or security feature documentation is added or modified.

Diff

diff --git a/sagemaker/latest/dg/debugger-built-in-rules-example.md b/sagemaker/latest/dg/debugger-built-in-rules-example.md
index 896987bf7..4e789025e 100644
--- a//sagemaker/latest/dg/debugger-built-in-rules-example.md
+++ b//sagemaker/latest/dg/debugger-built-in-rules-example.md
@@ -10,0 +11,4 @@ Debugger built-in rules example notebooksDebugger built-in rules example codeUse
+###### Note
+
+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). 
+
@@ -47,3 +50,0 @@ The following code sample shows how to set the Debugger built-in rules using the
-SageMaker Python SDK v3
-    
-    
@@ -86,29 +86,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    import sagemaker
-    from sagemaker.tensorflow import TensorFlow
-    from sagemaker.debugger import Rule, CollectionConfig, rule_configs
-    
-    # call built-in rules that you want to use.
-    **built_in_rules** =[ 
-                Rule.sagemaker(rule_configs.vanishing_gradient())
-                Rule.sagemaker(rule_configs.loss_not_decreasing())
-    ]
-    
-    # construct a SageMaker AI estimator with the Debugger built-in rules
-    sagemaker_estimator=TensorFlow(
-        entry_point='directory/to/your_training_script.py',
-        role=sm.get_execution_role(),
-        base_job_name='debugger-built-in-rules-demo',
-        instance_count=1,
-        instance_type="ml.p3.2xlarge",
-        framework_version="2.9.0",
-        py_version="py39",
-    
-        **# debugger-specific arguments below**
-        **rules=built_in_rules**
-    )
-    sagemaker_estimator.fit()
-
@@ -127,3 +98,0 @@ The following code example shows the structure of built-in rules to adjust param
-SageMaker Python SDK v3
-    
-    
@@ -187,47 +155,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    import sagemaker
-    from sagemaker.tensorflow import TensorFlow
-    from sagemaker.debugger import Rule, CollectionConfig, rule_configs
-    
-    # call the built-in rules and modify the CollectionConfig parameters
-    
-    base_job_name_prefix= 'smdebug-stalled-demo-' + str(int(time.time()))
-    
-    **built_in_rules_modified** =[
-        Rule.sagemaker(
-            base_config=rule_configs.stalled_training_rule(),
-            rule_parameters={
-                    'threshold': '120',
-                    'training_job_name_prefix': base_job_name_prefix,
-                    'stop_training_on_fire' : 'True'
-            }
-            collections_to_save=[ 
-                CollectionConfig(
-                    name="losses", 
-                    parameters={
-                          "train.save_interval": "50"
-                          "eval.save_interval": "10"
-                    } 
-                )
-            ]
-        )
-    ]
-    
-    # construct a SageMaker AI estimator with the modified Debugger built-in rule
-    sagemaker_estimator=TensorFlow(
-        entry_point='directory/to/your_training_script.py',
-        role=sm.get_execution_role(),
-        base_job_name=base_job_name_prefix,
-        instance_count=1,
-        instance_type="ml.p3.2xlarge",
-        framework_version="2.9.0",
-        py_version="py39",
-    
-        # debugger-specific arguments below
-        **rules=built_in_rules_modified**
-    )
-    sagemaker_estimator.fit()
-