AWS sagemaker documentation change

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

File: sagemaker/latest/dg/pipelines-local-mode.md

Summary

Removed legacy SageMaker Python SDK v2 code examples and redundant section headers

Security assessment

Cleanup of deprecated SDK examples without security implications. No evidence of security fixes or new security documentation.

Diff

diff --git a/sagemaker/latest/dg/pipelines-local-mode.md b/sagemaker/latest/dg/pipelines-local-mode.md
index 982d86ce1..ec79ae374 100644
--- a//sagemaker/latest/dg/pipelines-local-mode.md
+++ b//sagemaker/latest/dg/pipelines-local-mode.md
@@ -38,3 +37,0 @@ In order to execute a pipeline locally, the `sagemaker_session` fields associate
-SageMaker Python SDK v3
-    
-    
@@ -97,49 +93,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    from sagemaker.workflow.pipeline_context import LocalPipelineSession
-    from sagemaker.pytorch import PyTorch
-    from sagemaker.workflow.steps import TrainingStep
-    from sagemaker.workflow.pipeline import Pipeline
-    
-    local_pipeline_session = LocalPipelineSession()
-    
-    pytorch_estimator = PyTorch(
-        sagemaker_session=local_pipeline_session,
-        role=sagemaker.get_execution_role(),
-        instance_type="ml.c5.xlarge",
-        instance_count=1,
-        framework_version="1.8.0",
-        py_version="py36",
-        entry_point="./entry_point.py",
-    )
-    
-    step = TrainingStep(
-        name="MyTrainingStep",
-        step_args=pytorch_estimator.fit(
-            inputs=TrainingInput(s3_data="s3://amzn-s3-demo-bucket/my-data/train"),
-        )
-    )
-    
-    pipeline = Pipeline(
-        name="MyPipeline",
-        steps=[step],
-        sagemaker_session=local_pipeline_session
-    )
-    
-    pipeline.create(
-        role_arn=sagemaker.get_execution_role(), 
-        description="local pipeline example"
-    )
-    
-    # pipeline will execute locally
-    execution = pipeline.start()
-    
-    steps = execution.list_steps()
-    
-    training_job_name = steps['PipelineExecutionSteps'][0]['Metadata']['TrainingJob']['Arn']
-    
-    step_outputs = pipeline_session.sagemaker_client.describe_training_job(TrainingJobName = training_job_name)
-    
-