AWS sagemaker documentation change

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

File: sagemaker/latest/dg/pipelines-troubleshooting.md

Summary

Removed legacy SageMaker Python SDK v2 code examples and retained only v3 examples in the troubleshooting guide

Security assessment

Changes consist of deleting outdated SDK implementation examples. No security fixes, vulnerability patches, or security documentation additions are evident in the modifications.

Diff

diff --git a/sagemaker/latest/dg/pipelines-troubleshooting.md b/sagemaker/latest/dg/pipelines-troubleshooting.md
index 30d298bf3..eed88bc4f 100644
--- a//sagemaker/latest/dg/pipelines-troubleshooting.md
+++ b//sagemaker/latest/dg/pipelines-troubleshooting.md
@@ -55,3 +54,0 @@ You can either copy the script to the container or pass it via the `entry_point`
-SageMaker Python SDK v3
-    
-    
@@ -96,39 +92,0 @@ SageMaker Python SDK v3
-        base_job_name=f"{base_job_prefix}/pilot-train",
-        metric_definitions=[
-            {'Name': 'train:accuracy', 'Regex': 'accuracy_train=(.*?);'},
-            {'Name': 'validation:accuracy', 'Regex': 'accuracy_validation=(.*?);'}
-        ],
-    )
-
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    step_process = ProcessingStep(
-        name="PreprocessAbaloneData",
-        processor=sklearn_processor,
-        inputs = [
-            ProcessingInput(
-                input_name='dataset',
-                source=...,
-                destination="/opt/ml/processing/code",
-            )
-        ],
-        outputs=[
-            ProcessingOutput(output_name="train", source="/opt/ml/processing/train", destination = processed_data_path),
-            ProcessingOutput(output_name="validation", source="/opt/ml/processing/validation", destination = processed_data_path),
-            ProcessingOutput(output_name="test", source="/opt/ml/processing/test", destination = processed_data_path),
-        ],
-        code=os.path.join(BASE_DIR, "process.py"), ## Code is passed through an argument
-        cache_config = cache_config,
-        job_arguments = ['--input', 'arg1']
-    )
-    
-    sklearn_estimator = SKLearn(
-        entry_point=os.path.join(BASE_DIR, "train.py"), ## Code is passed through the entry_point
-        framework_version="0.23-1",
-        instance_type=training_instance_type,
-        role=role,
-        output_path=model_path, # New
-        sagemaker_session=sagemaker_session, # New
-        instance_count=1, # New