AWS sagemaker documentation change

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

File: sagemaker/latest/dg/processing-job-frameworks-tensorflow.md

Summary

Removed legacy SageMaker Python SDK v2 code example and v3 section header

Security assessment

Purely documentation maintenance removing obsolete examples. No evidence of security patches, vulnerability mitigations, or security feature documentation being added or modified.

Diff

diff --git a/sagemaker/latest/dg/processing-job-frameworks-tensorflow.md b/sagemaker/latest/dg/processing-job-frameworks-tensorflow.md
index 077b8c780..d12842c01 100644
--- a//sagemaker/latest/dg/processing-job-frameworks-tensorflow.md
+++ b//sagemaker/latest/dg/processing-job-frameworks-tensorflow.md
@@ -13,3 +12,0 @@ The following code example shows how you can run your Processing job using a Doc
-SageMaker Python SDK v3
-    
-    
@@ -43,43 +39,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    from sagemaker.tensorflow import TensorFlowProcessor
-    from sagemaker.processing import ProcessingInput, ProcessingOutput
-    from sagemaker import get_execution_role
-    
-    #Initialize the TensorFlowProcessor
-    tp = TensorFlowProcessor(
-        framework_version='2.3',
-        role=get_execution_role(),
-        instance_type='ml.m5.xlarge',
-        instance_count=1,
-        base_job_name='frameworkprocessor-TF',
-        py_version='py37'
-    )
-    
-    #Run the processing job
-    tp.run(
-        code='processing-script.py',
-        source_dir='scripts',
-        inputs=[
-            ProcessingInput(
-                input_name='data',
-                source=f's3://{BUCKET}/{S3_INPUT_PATH}',
-                destination='/opt/ml/processing/input/data'
-            ),
-            ProcessingInput(
-                input_name='model',
-                source=f's3://{BUCKET}/{S3_PATH_TO_MODEL}',
-                destination='/opt/ml/processing/input/model'
-            )
-        ],
-        outputs=[
-            ProcessingOutput(
-                output_name='predictions',
-                source='/opt/ml/processing/output',
-                destination=f's3://{BUCKET}/{S3_OUTPUT_PATH}'
-            )
-        ]
-    )
-